If you need a good Windows laptop for work, college or general home use, Dell's 15.6" 2K Touchscreen Laptop is currently $600 (was $1100) at Best Buy.

Designed to comfortably handle everyday tasks, its 13th Gen Intel Core i7-1355U has 10 cores and can boost to 5GHz, backed by 16GB of DDR5-4400 memory and a 512GB PCIe SSD. The 15.6-inch IPS touchscreen gives you a large working area, and a dedicated numeric keypad makes it particularly useful for spreadsheets, accounts and other business or college work involving lots of numbers.

Why we recommend it

The combination of a Core i7 processor, 16GB DDR5 memory and a 512GB SSD gives this Dell a well-balanced specification for everyday use.

Alongside Wi-Fi 6 and Bluetooth, there are USB-C 3.2, USB-A 3.2 and USB-A 2.0 ports, HDMI 1.4, an SD card reader and a headphone jack. The full-size keyboard and numeric keypad also make it a sensible choice for business and college work.

At 4.28 pounds, it's reasonably portable for a full-size 15.6-inch laptop, although this is more of an everyday workhorse than an ultraportable.

Price context & historical value

Best Buy has slashed the price of this Dell laptop from $1,099.99 to $599.99, saving you $500, a substantial cut on a laptop with a Core i7 processor, 16GB of DDR5 RAM and a 512GB SSD.

At its $1,099.99 regular price, several of the compromises would be harder to overlook, particularly the basic integrated graphics, 220-nit display and lack of a backlit keyboard. At $599.99, it's a good all-rounder for someone primarily interested in productivity and everyday computing tasks.

Should you buy it?

✅ Buy the Dell 15.6-inch touchscreen laptop if...

You want a full-size laptop for business, studying, home working or everyday computing. The Core i7 processor provides plenty of performance for office applications, web browsing, video calls and other everyday workloads.

The touchscreen adds another way to navigate Windows, and the separate numeric keypad is useful if you spend much of your day working with spreadsheets or figures. Its 15.6-inch screen also provides considerably more workspace than smaller 13-inch and 14-inch laptops.

❌ Skip the Dell 15.6-inch touchscreen laptop if...

You need serious graphics performance. This model relies on Intel UHD integrated graphics, so it isn't designed for demanding gaming, 3D rendering or GPU-heavy creative workloads.

The Catch: What to know before you buy

The main compromise is that this isn't a laptop for demanding graphics-heavy creative work. Intel UHD integrated graphics are fine for everyday productivity and media, but you'll want a dedicated GPU for more intensive visual workloads.

The 220-nit display is also relatively modest in brightness, particularly if you plan to work outdoors or in brightly lit rooms. There's no backlit keyboard either, which is worth bearing in mind if you regularly work after dark.

• Shop the full 60th anniversary sale at Best Buy

The reMarkable line is some of the best e-ink handwriting tablets we've ever tested - our hardware editor uses his collection every day for taking digital notes. And right now, the 11.8in reMarkable Paper Pro is down to $699 (was $799) and 7.3in reMarkable Paper Pro Move now $469 (was $569) at Best Buy,

The full-size reMarkable Paper Pro includes the Marker Plus and Premium Leather Book Folio. Its 11.8-inch color display gives you plenty of room for handwritten notes, PDFs, sketches and ebooks, making it the better choice if you mainly work at a desk or want the closest thing to a large digital notebook.

The reMarkable Paper Pro Move drops from $569 to $469 and also includes the Marker Plus, along with a Mosaic Weave Book Folio. Its 7.3-inch Canvas Color e-ink display fits into a device smaller than a paperback, so this is the one to choose if you want something you can easily carry with you.

Why we recommend it

Both devices solve the same problem in very different ways. In his four-star review of the Paper Pro, our expert Philip said: "This is a luxury tablet, so it’s the most expensive in its class, but it feels as premium as it looks, especially if you buy a nice Book Folio." He also called it an "incredibly well-built device, and it has a premium finish and design that is truly… impeccable."

In his five-star review of the Paper Pro Move, Philip said it was "the rare gadget that gives me exactly what I wished for, nothing more and nothing less." He went on to add that: "The reMarkable Paper Pro Move is a thin and very lightweight writing tablet that feels incredibly solid for its subtle heft. I had no trouble holding the tablet in the palm of my left hand and writing comfortably across the whole page with my right. Then I just tucked the tablet into the back pocket of my jeans."

Price context & historical value

Best Buy has slashed $100 off both models. The reMarkable Paper Pro drops from $799 to $699, while the Paper Pro Move falls from $569 to $469. We haven't seen either of these exact bundles sell for less.

The identical saving doesn't make them equally expensive, of course. The Move remains $230 cheaper and is the more accessible option if you mainly want handwritten notes on the go. The full-size Paper Pro costs considerably more, but you're paying for its much larger 11.8-inch display and much larger working area.

Which should you buy?

✅ Buy the reMarkable Paper Pro if...

You want the larger canvas. The 11.8-inch display gives you far more room for handwritten notes, detailed sketches and full-page PDFs, so it makes the most sense for creators, students and professionals who expect to spend long periods working on the device.

It's also the better option if you're trying to replace a conventional notebook or pad on your desk. The larger screen makes it easier to see more of a document at once.

✅ Buy the reMarkable Paper Pro Move if...

Portability matters more than screen size. At 7.3 inches and 8.16 ounces, the Paper Pro Move is much easier to carry around and is small enough to slip into a bag or even a large jacket pocket.

It still gives you handwriting-to-text conversion, searchable handwritten notes, cloud syncing and a paper-like color display and a two-week battery life.

The Catch: What to know before you buy

Neither of these should be treated as a conventional Android or iPad-style tablet. Their lack of apps and distractions is central to the appeal, but it also means they can't be used for tasks like video streaming, games, social media or apps.

The choice between them really comes down to screen size versus portability. If PDFs, sketches and large pages are important, the Paper Pro's bigger display is worth having. If you primarily take notes in meetings or while on the go, the Paper Pro Move is a better choice and much easier to carry.

• Shop the full 60th anniversary sale at Best Buy

• Hostinger launches AI Builder, moving beyond initial web design to help users set up and run backends, ecommerce, and marketing automatically
• AI Builder was driven by demand, with non-traditional builds like SaaS, apps, and internal tools growing fivefold in the last 12 months
• The new tool benefits from Hostinger’s infrastructure, with no third-party setup needed for many projects.

Top web hosting Hostinger has announced the launch of its new AI Builder platform, which aims to move beyond AI-generated websites by combining AI-led creation and visual control with backend infrastructure, ecommerce, embedded AI, and marketing.

The company says taking this approach aims to shift AI’s involvement from generating a first version to setting up and operating the systems behind a project.

The new tool was launched following a noticeable change in what Hostinger customers are building, highlighting research which found a year ago fewer than 4% of projects created on its platform fell outside of the traditional website category. More recently, nearly one-in-five projects are software-as-a-service (SaaS) products, learning platforms, and internal tools, a fivefold increase in the past 12 months.

More than a traditional website

SaaS projects of this type require more than a traditional website. Hostinger’s new tool will help users deal with everything from authentication and data to infrastructure. It will also help businesses find, reach, and keep new customers.

Where many AI platforms help you build a tool, then leave it to the user to assemble the components and deal with the infrastructure themselves, Hostinger benefits from built-in backend, marketing, and ecommerce systems.

"The next battle in online presence is not about who can generate a homepage in 30 seconds," said Auksė Žirgulė, Vice President of Product at Hostinger.

"It is about what happens next. Can the AI open the store, create the user management system, help attract customers, and keep improving the project? That's the direction we're taking with AI Builder."

AI Builder goes beyond the usual ‘prompt to build’ approach, allowing users to upload a whole range of content as a starting point. For example, a restaurant may upload a menu PDF, a designer may upload a screenshot or reference image, and a business owner can paste the link to an outdated website they want to modernize.

Hostinger’s AI Builder is now available globally to new and existing customers. For more information and to get started, you can visit the AI Builder page.

• Geekom has admitted a software driver contained malware
• A LAN driver on a legacy page was hosting the Asruex backdoor
• The malware can track keystrokes, steal passwords, and intercept data

Hardware maker Geekom has admitted that a network driver for multiple mini PC variants hosted Asruex backdoor malware, potentially putting users at risk

The LAN driver for Geekom’s range of A7, A8, AE7, AE8, AX7 Pro and AX8 Pro mini-PCs hosted the malicious package with administrator-level permissions that allowed it to monitor everything you type, steal data, and even swipe passwords from your machine. The malicious software also connects to a command and control (C2) network to send and receive information from hackers.

Geekom has issued an apology and removed the software package in question, but if you have a Geekom mini PC from the aforementioned range and have installed the LAN driver, I’d definitely recommend doing a full system virus scan, with a wipe and reset just to be sure.

Geekom ships malware-riddled LAN driver

Videocardz first broke the story after investigating claims from a Reddit user who reported finding a malicious executable file contained within the LAN driver.

Videocardz then independently investigated the claim using FileScan.IO, MetaDefender VirusTotal, and YARAify. Each antivirus engine detected the executable as malicious.

In Geekom’s statement about the malicious file, the company said that the driver was hosted on a “legacy page [that] had already been replaced and was no longer accessible through the normal Support navigation, although it remained indexed by search engines.”

So when users searched for the LAN driver using Google, the result that came up was the malicious file. I always recommend users install drivers and other software from the official distributor rather than using Google listings as hackers can use tactics such as SEO poisoning or promoted pages to offer dodgy software. But in this case the legacy page was official.

Geekom has confirmed that none of its mini PC range were shipped with the malicious driver preinstalled, so if you haven’t directly downloaded the malicious software from the legacy page, you should be okay. But consider running a Windows Defender scan to be sure.

In order to guarantee that your mini PC is free of the malicious driver, perform a complete wipe and reset of Windows, and install a new Windows image direct from Microsoft’s official page. Going forward, only install software and drivers from the official support pages of the manufacturer.

• Heights Finance breach exposed sensitive customer data via a compromised third‑party cloud platform
• Stolen records included contact details, financial info, and government identifiers
• Over 700,000 Texans affected; company offers credit monitoring and identity protection

US loan company Heights Finance has revealed it suffered a cyberattack earlier in 2026 in which it lost sensitive data on hundreds of thousands of its customers.

The company published a data breach notification on its website, disclosing that on May 7 2026, it saw an “unauthorized actor” gaining access to a cloud-based platform, hosted by a third party, which the company uses to store certain customer data.

The breach was limited to that cloud platform only and did not affect its loan management system, or other systems and networks.

At least 700,000 victims

As is standard practice in these incidents, Heights Finance notified the relevant authorities and brought in outside cybersecurity help.

The subsequent investigation determined that the attackers - which were not named - stole contact details (names, postal addresses, phone numbers, email addresses), financial information (account details, bank account information such as bank name, account number, routing number), government identifiers (Social Security numbers, tax IDs, driver’s license numbers), and other miscellaneous data.

“Your information may be involved if you received a loan through Heights, or if you inquired about or applied for a loan product (including through a third party),” the company said. “Your information may also be involved if you were a former borrower of Curo Management or any of its former or current related brands.”

The exact number of affected individuals is not known at this time. Heights Finance told regulators in Texas that the breach affected more than 730,000 of its residents, and added that it affected those living in Alabama, Tennessee, Georgia, Texas and South Carolina.

We don’t know which cloud-based platform Heights Finance is using, and the threat actors are yet to claim responsibility for the attack. In the meantime, the company is offering affected customers credit monitoring and identity protection services through Epiq.

Via The Record

Enterprise AI is reaching an important economic turning point.

For the past two years, organizations have largely evaluated AI through the lens of token pricing and model capability.

As AI moves from experimentation into business-critical operations, that approach is becoming increasingly incomplete.

The question is no longer simply what each token costs, but what it costs to deliver AI capability that is affordable, sustainable and commercially predictable at enterprise scale.

Every AI interaction ultimately depends upon physical IT infrastructure, consuming compute, memory, networking, electricity and cooling regardless of how those costs are presented to the customer.

Understanding the economics of that infrastructure is becoming just as important as understanding the capabilities of the models themselves.

Organizations that focus only on the price of a token risk overlooking the factors that will ultimately determine the long-term cost, resilience and sustainability of enterprise AI.

Why the price on the invoice isn't the whole story

Organizations naturally focus on the invoice because it represents the visible cost of AI. Consumption-based pricing appears straightforward, transparent and easy to compare. Yet every token reflects far more than access to a language model. Behind every AI interaction sits physical infrastructure consuming compute, memory, networking, electricity and cooling.

Those infrastructure costs remain largely invisible to the customer despite having a direct influence on the economics of enterprise AI. As AI moves from isolated pilots into production workloads, infrastructure decisions are repeated across millions of inference requests, making their long-term commercial impact increasingly significant.

Understanding enterprise AI therefore requires organizations to look beyond the invoice. The efficiency, resilience and operating characteristics of the infrastructure delivering AI capability increasingly determine what AI will cost over its operational lifetime.

Infrastructure efficiency is becoming a competitive advantage

The architecture underpinning an AI platform has a significant influence on its economics over time. Purpose-built inference infrastructure can significantly improve energy efficiency compared with architectures optimized primarily for AI training workloads, often without requiring complex liquid cooling.

Those efficiency gains have important commercial consequences. Lower energy demand reduces cooling requirements, simplifies facility design and lowers operating costs throughout the lifetime of the infrastructure. At enterprise scale, even relatively small efficiency improvements become commercially significant when repeated across millions of inference requests.

Consumption-based, token-metered AI remains an appropriate deployment model for organizations with variable or exploratory workloads. As AI becomes embedded within everyday business operations, however, many organizations are finding that continually increasing token consumption creates an operational cost model that becomes progressively harder to forecast and control.

Token volumes measure the level of AI activity, but they do not measure the business value created by that activity. Enterprise leaders are therefore becoming increasingly focused not simply on the cost of consuming AI, but on the long-term economics of delivering AI capability in a commercially sustainable way.

Dedicated inference infrastructure represents a different economic model. Rather than paying for every interaction, organizations invest in AI capability with predictable operating costs, greater control over performance, data location and operational resilience. The discussion therefore shifts from purchasing tokens to building sustainable AI capability.

Where that infrastructure is combined with on-site renewable generation and long- duration energy storage, organizations can further improve cost predictability while strengthening operational resilience and supporting long-term sustainability objectives.

A broader conversation about enterprise AI economics

Enterprise AI is entering a more mature phase of adoption. The discussion is no longer centered solely on model capability or the cost of individual tokens. Increasingly, organizations are asking how AI can create measurable business value while remaining commercially sustainable over the long term.

That changes the conversation in the boardroom. Success is no longer measured simply by the volume of AI consumed, but by the outcomes it delivers. Token usage may indicate the level of AI activity, but it does not measure the value created for the organization. The focus therefore shifts towards deploying AI capability that delivers predictable commercial returns, operational resilience and strategic advantage.

As organizations become increasingly dependent upon AI, they must also recognize that the underlying models are not static. Foundation models continue to evolve through incremental updates and refinements, many of which may be difficult for users to detect but can nonetheless influence behavior and outputs. Enterprise AI therefore requires governance that extends beyond monitoring consumption.

Organizations need confidence that they understand not only what AI costs to operate, but also how the capability itself is changing over time and what those changes mean for business performance, compliance and risk. The ability to govern both the economics and the evolution of AI is becoming a strategic capability in its own right.

Infrastructure remains the foundation that enables those outcomes, but it is no longer the destination of the discussion. The real objective is to create AI capability that delivers measurable business value, remains commercially sustainable and can be governed with confidence as technologies continue to evolve.

Organizations that understand the relationship between infrastructure, operating economics, governance and business value will be better placed to realize the long-term benefits of enterprise AI.

We've reviewed, rated, and ranked the best productivity tools.

This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

The demo that convinced me happened at an art exhibition I'd built myself.

We couldn't afford a team of docents, so I put together a side project to stand in for one. There was no prompt box and nothing to type. You pointed your phone at something and it told you what you were looking at. Then the show closed, and people kept using it anyway.

They pointed it at buildings, flowers, their kids' toys, posters on the street, none of which had anything to do with the exhibition.

Without anyone asking them to, they had decided a camera should be able to explain the world back to them.

That expectation is now the industry's to deliver on, and it arrives just as AI shifts from a training problem to an inference one, with more of the real work happening on-device while someone points a phone at an uncooperative real world.

A model needs different things from a frame than your eye does: edges to lock onto, and structure it can recover when the shot is full of glare or blur. The color and contrast that make a photo look good are, to the model, mostly in the way.

The sensor you'd want for a human viewer and the one you'd want for a model call for different designs.

The sensor has become a perception problem, and the people building real-time vision systems now care about it the way photographers once did.

Where the sensor came from

Eric Fossum's CMOS active-pixel sensor, developed in the early 1990s, is why a capable camera now sits in nearly every pocket.

His later work heads somewhere else entirely: the Quanta Image Sensor, which counts individual photons. That arc previews what is happening to AI now: it runs from capturing a frame a person will like toward capturing the cleanest signal for a machine to reason over.

The sensor is turning into an instrument of perception, and photography is becoming only one of the things it is for.

Why inference changes the job

Inference is what makes the problem urgent. Back when AI was mostly about training, the camera's role was indirect: it produced data to be labeled and learned from later, at leisure. Inference is not leisurely. It happens live, on a device short on power and already running warm.

When a system has to recognize something while you're still pointing at it and answer before the moment passes, the weakest link is whatever reaches the model, and it sets the ceiling on everything the product can do.

Part of the industry is still betting on the wrong thing. The assumption is that a big enough model will clean up whatever the camera hands it. I've watched this hold up product roadmaps for two years running, and I don't think it survives contact with physics.

No amount of model size recovers detail that motion blur or glare already destroyed, or that a beauty-first pipeline discarded before the model ever saw the frame. The bottleneck is moving upstream, toward the sensor and what sits between it and the model.

What the hardware is already doing

Some sensors now do part of the processing on-chip, so the first computations happen before data leaves the pixel array. Event-based sensors, sometimes called neuromorphic, register only the parts of a scene that change, which suits real-time perception far better than streaming whole frames. Global shutters help too, by cutting the motion artifacts that wreck machine reading.

Even high dynamic range, long tuned to make skies dramatic, is being retuned around what a model can read cleanly in high contrast. Different as these are, they point the same way, toward a version of the world a model can work with.

From recognition to reasoning

The obvious thing to build is visual search: you point at something and get a label back. Point your phone at a menu, though, and what you want is help deciding what to eat; point it at a poster, and the useful move might be saving the event to your calendar. Recognizing the object is only the starting condition. The product lives in knowing what should happen next. That move is where the sensor becomes the first link in a longer chain of interpretation, beyond simple capture.

I never meant to build a camera product at all; it began as a cheap way to avoid hiring docents for one exhibition. But that small project taught me the instinct is already out there. The next camera race will run across the entire stack: the sensor, the image pipeline, the edge compute, the model orchestration, and what the system does with an answer.

Device makers have spent years perfecting image quality, and they will have to take inference quality just as seriously. And whoever writes the software can't treat capture as someone else's problem: how a signal is grabbed and routed shapes the result long before a user sees it. The old boundary between hardware and software matters less the further you push into real-time perception.

Startups still have room here. Their advantage is speed of definition: framing a problem before the larger players finish arguing about it. Generic model capability, on its own, is getting harder to defend. The open question is what happens once a single system can see, reason about what's in front of it, and act on the same event, and who gets there first with a product that holds together.

Human eyes vs machine minds

My bet is that the next few years will sort companies into two camps: the ones optimizing for human eyes and the ones optimizing for what a machine has to read. Inside a flagship phone, those goals still overlap enough to ignore the difference.

As a strategy, they are already diverging. The more durable position belongs to whoever can turn the messy real world into the cleanest signal for a machine to reason over.

And that starts at the sensor.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

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• Hacker “TheHatman” claims to have stolen millions of Azure/Entra employee records from major firms
• Data includes names, emails, job titles, privileged accounts; risks include impersonation and fraud
• Victims dispute scope, but researchers say infostealer‑based theft makes the leaks likely authentic

A cybercriminal is selling millions of user records on the dark web, which they claim to have stolen from large organizations such as McDonalds, Tata Consultancy Services, and Wyndham Hotels.

A hacker going by the alias “TheHatman” posted multiple threads on dark web forums, claiming to have stolen information from Azure and Entra environments.

TheHatman said they broke in using compromised login credentials, targeting almost a dozen organizations.

What was stolen and from whom?

Among the victims and the number of records exposed, are:

McDonald’s Corporation: 1,700,000 records
TCS (Tata Consultancy Services): 800,000 records
Vodafone: 425,000 records
HCL Technologies: 250,000 records
InterContinental Hotels Group (IHG): 185,000 records
Kyndryl: 170,000 records
Gap Inc.: 80,000 records
Hexaware Technologies: 20,000 records
Wyndham Hotels: 9,000 records

They are now looking for a buyer: “I’m selling McDonald’s Corporation internal employee dump downloaded directly from Azure Tenant using compromised credentials,” TheHatman said in one of the posts.

In their writeup, security researchers from Cybernews said they analyzed one of the samples posted on the dark web and said the entries were “consistent with Azure directory exports”.

They contained employee names, emails, phone numbers, job titles, workplace addresses, IDs, the departments they work in, user group memberships, service accounts, and highly privileged account records.

What are the risks?

Stealing information such as names, email addresses, and workplace details might not sound like a worrisome breach of privacy, but the implications are rather big. Cybercriminals can use it to impersonate a business partner or a major client, and try to trick their employees into installing ransomware, or making a fraudulent wire transaction. That way, they can escalate what seems like a relatively benign breach, into a full-blown cyberattack with material and legal consequences.

For example, a criminal might discover a Vodafone employee that regularly handles payments to a particular supplier. They might impersonate that supplier’s finance director, engage in conversation and, while requesting a new payment, warn that the company changed their bank account. This is not a purely theoretical scenario - it’s been documented time and time again.

What did the victims say?

Most organizations are yet to give an official statement about these claims. Gap told BleepingComputer that it found no evidence of the breach and suggested that the attackers merely repackaged data from an older incident.

“Our preliminary investigation indicates that the data in question is limited in scope, non-sensitive and dated back to several years ago. Notably, there is no evidence to suggest that our corporate systems have been compromised,” Gap told the publication.

Tata Consultancy Services notified the Indian National Stock Exchange about the breach last week, also suggesting that this was a resurfacing of an older incident.

“The Company has investigated the matter and has not found any credible evidence of a breach of TCS systems or customer environments,” TCS said in the filing. “The information referenced appears to be more than four years old and limited to basic employee information. There is no indication that customer data, customer systems, or TCS operational systems have been impacted.”

TCS said the attackers broke in using credential stuffing, something that could have only been done years ago: “The attacker claims to have used password spray and Multi-Factor Authentication (MFA) fatigue as the attack vector. The Company has had strong safeguards in place against such techniques for more than two years.”

Not everyone agrees with that assessment, though. Security researchers Hudson Rock believe the attackers stole login credentials with an infostealer, rather than through password spraying.

“Judging by the massive size of the organizations impacted, it appears highly likely that this campaign originates from targeted exploitation of Infostealer infections rather than a systemic zero-day vulnerability in Azure,” the researchers said in their report. “If this were a widespread vulnerability, we would likely see a much broader spectrum of organizations impacted, including smaller businesses, rather than just these massive Fortune 500-level enterprises.”

Hudson Rock also described the stolen data as “likely highly authentic”, hinting that just because it’s older, it doesn’t mean it’s not useful.

Whether you're looking for a cheap printer for back-to-school or upgrading your office setup, now is a great time to buy. I'm tracking all the best printer deals on some of our top-performing inkjet, ink tank, and laser printers.

For most home, dorm room, and home office use, I recommend ink tank printers. They work the same as inkjet models, sspraying liquid ink on the page, but the bottled ink refills are much cheaper than cartridges, and companies like Epson, HP, and Canon tend to include free ink in the box to save you even more money.

For business use, a laser printer is the best pick for fast, high-volume document printing where the text is sharp and detailed. However, some higher-grade ink tank printers come close for small businesses. You'll find my favorite printer deals below, including some of the best home printers and best ink tank printers we've tested.

Best printer deals: Editor's picks

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• Chinese government moves ahead with Windows 10 replacement
• Heavily customized Windows edition will be replaced by domestic alternative
• Windows 10 official end of life was October 14, 2024

The Chinese government has revealed plans to finally remove Windows 10 from its systems as it looks to move towards home-made alternatives.

A report from Bloomberg claims the Chinese Ministry of State Security has told some state-linked entities to start uninstalling the software as part of a multi-year plan to remove dependence on Western technology.

The version of Windows 10 is developed by C&M Information Technologies Co (CMIT) which was formed in 2016 as a joint venture between Microsoft and the state-owned China Electronics Technology Group Corp in a bid to make sure Windows 10 was compliant with the government's security requirements.

A welcome change?

There's no official confirmation as to why the decision has been made now, with CMIT reportedly planning to retire the software in February 2027, in line with a government campaign to curb the use of foreign technology.

Bloomberg notes the move comes just weeks before US President Donald Trump is set to meet with Chinese premier Xi Jinping, where security concerns will no doubt be high.

“Microsoft is not aware of a security incident affecting this product, which continues to receive regular security updates,” a spokesperson for the US company told Bloomberg. “We have nothing further to share.”

Windows 10 famously saw its end of life in October 2024, as Microsoft retired the software in order to focus on newer editions.

However some users were initially able to get extra support until 2026, a deadline which was surprisingly extended even further recently, with Microsoft's Extended Security Updates (ESU) scheme now indicating support will run through to October 2027.

The move was slow to catch on, as a study in July 2026 found one in six PCs still ran the older software as opposed to 78.8% for Windows 11 - with healthcare and pharmaceuticals (23%), consumer and retails (23%) and manufacturing (18%) among the industries most likely to still be running Windows 10, with SMBs (21.4%) more likely to be running the outgoing OS compared with enterprises (16.6%).

This is especially concerning as the average Windows 10 device has around three times as many active CVEs (1,903) as a Windows 11 devices (652).

Software security was built around human development.

People wrote, reviewed and deployed code. Now machines are taking over.

In a recent paper, Anthropic reports that more than 80% of the code merged into its production codebase is authored by their AI model, Claude.

The same capabilities that make developers more productive are changing the economics of cyberattacks.

While adversaries still define the objective, machines can generate the payloads, test variants, adapt code to different environments and repeat the process at a velocity that security programs can’t match.

Speed is Marginalizing Security Controls

Most enterprise software security workflows assume there is time for review. Code is written, scanned, tested, approved and deployed. If something suspicious happens later, security teams investigate and respond.

That model breaks down when software moves from prompt to execution in minutes.

AI-generated code can become a script, dependency, automation job or infrastructure change almost immediately. While development agents can modify files, resolve packages and run commands.

Human reviewers are no longer in the loop.

Attackers can use the same mechanics to generate exploits, test evasion techniques and adjust payload behavior for different targets. This creates more variation with fewer stable indicators for defenders to recognize.

While AI-assisted analysis can improve triage, it still often produces probability, not policy. At machine speed, “probably suspicious” is not good enough.

Machines Change The Attack Model

Human attackers are not disappearing. But more of the attack chain is becoming machine-executed.

AI can automate reconnaissance, accelerate vulnerability discovery, generate exploit code, rewrite payloads and adapt command sequences to the target environment. But most defensive measures are designed around human constraints: reused infrastructure, shortcuts and trackable patterns. These don’t apply to machine attacks.

A machine-generated payload may not match a known signature or have an established reputation. It may be created, used briefly and discarded. But AI malware must still interact with the target environment to achieve its objective. Its behavior cannot conceal its intent, since it must access resources and change the environment in ways that advance the attack.

What malicious code is capable of doing is the more durable security signal.

Security Needs to Ask A Different Question

Software supply chain security has improved, but much of it still validates the artifact’s properties before execution rather than governing execution itself.

SBOMs, signing and provenance give security teams greater confidence in a code's composition, origin and build history. But knowing where software came from does not reveal what it will do when it runs.

Software can pass each of those checks and still create risk. Even an artifact produced through a legitimate build process may violate policy at runtime, while an AI-generated script may complete its intended task in a way that exposes data or systems. As a result, a clean dependency list is not proof of safe behavior.

Post-Execution Detection Is Too Late

Detection and response remain essential, but they intervene after risk has entered the environment. By the time suspicious behavior is visible, software may have accessed secrets, changed system state, opened network connections or created persistence.

AI compresses that window. Code can be generated, modified and deployed faster than humans can review it. Waiting for post-execution evidence gives attackers too much room to operate.

We need to shift the decision point left. Instead of asking, “Can we contain this software if it behaves badly?” the question should be, “Should this behavior be permitted to execute in the first place?”

That does not mean replacing existing controls, but rather changing where the decisive security gate sits.

Zero Trust for Code

Zero Trust changed enterprise security by rejecting implicit trust. Users, devices, sessions and access requests are not trusted simply because they appear familiar. They must be verified against policy.

Software execution needs the same level of verification.

Code should not be trusted solely because it came from a known repository, was signed by a recognized publisher, passed through a build pipeline or has not been seen exhibiting malicious behavior before. Those are useful indicators, but they are not conclusive.

Zero Trust for Code addresses this problem. Before software runs, its expected behavior should be evaluated against policy. If the behavior is acceptable, execution can proceed. If not, the artifact should be blocked, restricted, isolated or escalated for review.

Organizations can start by mapping every path through which code enters the environment or executes with meaningful privilege. This includes formal development channels such as repositories, open-source packages, containers and CI/CD pipelines, as well as email attachments, downloaded files, macros, browser extensions, endpoint installers, third-party integrations and scripts introduced through AI or automation tools.

Then identify where those paths rely on inherited trust. If execution is allowed because software came from an approved source, was signed, passed through a build process or has no malicious history, the control is incomplete. Behavior still has to be evaluated before the artifact is allowed to run.

As AI takes on more of the work of creating legitimate and malicious code, enterprises can no longer assume that code which clears existing checks should be allowed to run. Execution must become a deliberate security decision.

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• Pokémon Center UK orders disrupted after CEVA Logistics cyberattack on July 30 2026
• Customer names, addresses, emails, and order details likely exposed, but accounts and payments safe
• Around a dozen organizations confirmed affected; no group has claimed responsibility yet

Customers who recently ordered their favorite Pikachu toy from Pokémon Center might have to do it all over again, since the company suffered a third-party cyberattack which disrupted its operations.

The official store for Pokémon merchandise in the UK has reached out to its customers via email to warn them about a recent cyberattack and its consequences.

According to BleepingComputer, which has seen a copy of the email, the company told its customers they had to “cancel your recent order due to an unforeseen fulfilment issue”.

CEVA Logistics

The company’s website is also showing a notification saying the company is “currently experiencing delays affecting some orders for our UK customers.”

“These orders may take longer than usual to process, dispatch, and deliver. We apologize for the inconvenience and appreciate your patience.”

The company said the attack struck its logistics provider, CEVA Logistics.

"CEVA Logistics, the vendor Pokémon Center utilizes to ship products from PokemonCenter.com for customers in the United Kingdom and Germany, has informed us that unfortunately they were a victim of a cyber attack commencing on 30 July, 2026."

Last week, one of the biggest shipping and logistics companies in the world disclosed an incident that forced it to shut down parts of its IT infrastructure and affected eight warehouses. At the time, a handful of its customers reported being affected by the breach, including Dutch retailers Bol and De Bijenkorf, and PC gaming powerhouse Valve.

Pokémon Center said the data most likely exposed in this incident includes people’s full names, mailing addresses, phone numbers, email addresses, and details about what they previously ordered on the site. User accounts are apparently safe, and so are payment details.

So far, around a dozen organizations are confirmed as having been affected. No threat actors have claimed responsibility yet.

For years, cybersecurity has become increasingly measurable. Security leaders can often tell you how long it takes to detect an intrusion, contain an attack and restore normal operation. Those figures have given boards a straightforward way to judge progress, offering reassurance that investment in security is delivering real improvements.

Metrics such as Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) have earned their place at the table. They both provide a clear picture of how effectively security teams perform when something goes wrong and have helped drive better incident response across the industry.

The problem is not that these metrics are wrong. They were designed for a different era, when technology changed more slowly, attack methods evolved over longer timescales and AI wasn't yet part of the equation.

Today's businesses are introducing new technologies at an extraordinary pace. AI is becoming embedded across organizations, cloud environments continue to expand and businesses are more interconnected than ever before. At the same time, attackers are constantly adapting their own techniques, taking advantage of new tactics and tools almost as quickly as they emerge.

CISO’s and boards need to dynamically review the changing threat landscape and risk posture and ask themselves whether the metrics relied on for years still tell us everything we need to know.

Mind the gap

Every business wants to detect attacks sooner, contain them faster and recover with minimal disruption. That’s why MTTD and MTTR remain valuable operational measures. They tell us how effectively a security team performed once an incident was underway.

What they don't tell us is whether the business is becoming better prepared for what comes next, more resilient, more agile in recovery. That matters because cyber risk continues to evolve long after an incident has been contained.

The UK Government's Cyber Security Breaches Survey 2025/2026 found that 43% of UK businesses experienced a cyber breach or attack during the previous year.

This reinforces how security teams are operating in an environment where incidents are a regular reality, whether it's in their own environment, or that of one of their supply chain. Responding well is important, but resilience is shaped by everything that happens before incidents.

A business may recover quickly from an attack but still take months to review its security policies, reassess supplier risk or strengthen controls in response to what it has learned. By the time those changes are made, the threat landscape will have moved on.

Traditional metrics tell us how quickly a business responds to an incident. They don't tell us how quickly it learns from one, or how quickly it adapts.

Closing the gap

If we're going to close that gap of preparedness, our metrics need to evolve as well. Resilience is no longer defined solely by how well a business responds to isolated incidents, but by how quickly it keeps pace with continuous change.

I believe organizations should start thinking about another benchmark alongside the ones we already know: Mean Time to Adapt (MTTA).

MTTA considers how long it takes to recognize a meaningful change in the threat landscape and turn that knowledge into action.

Sometimes that action will be technical. It could mean updating the rules security tools used to detect emerging attack techniques. Or it might involve tightening access to critical systems after a serious vulnerability is discovered. It may also be a proactive lessons learned view of an attack on another organization or sector to understand how vulnerable the organization would be.

In other cases, the response will be organizational rather than technical. It may involve reviewing governance, changing how cyber risk is reported to the board or refreshing employee awareness programs to reflect the latest tactics being used by attackers.

Either way, resilience depends on both. The strongest security programs combine technical improvements with organizational change, ensuring businesses can recognize change and act on it quickly.

That’s why closing this gap is not only a technology challenge. It relies on decision-making, leadership and a willingness to keep questioning whether existing assumptions still hold true. Businesses that adapt well rarely assume their current security program is finished. They expect it to evolve because the environment around them is evolving too.

That thinking is increasingly reflected across the wider industry. For example, the National Cyber Security Centre's Cyber Assessment Framework places governance, risk management and continual improvement at the heart of cyber resilience. It recognizes that security is an ongoing organizational capability, not a one-time achievement.

A different conversation in the boardroom

If preparedness and adaptation becomes a more meaningful measure of resilience, it will change the conversations taking place in the boardroom.

Most directors already receive regular updates covering incidents, phishing activity and response times. Those reports remain important, but they won’t always show how well the business is responding to change itself.

The discussion must now move beyond operational reporting and give greater prominence to MTTA. This would give boards a way to measure how quickly an organization responds to change, rather than simply how efficiently it handles incidents.

In practice, that means asking a different set of questions. How quickly does the business reassess risk when a significant new threat emerges? How long does it take for new intelligence to shape security policies? Have lessons from recent attacks fundamentally changed the way the organization operates, or have they simply been recorded and filed away?

By measuring adaptation, rather than response alone, organizations can answer these questions with greater confidence and build a broader picture of resilience.

And this isn't solely a question for security teams. It depends on leadership, governance and how prepared the wider business is to make decisions as risks continue to evolve.

Measuring what matters

MTTD and MTTR will remain valuable measures of operational performance. But if organizations want to understand how resilient they really are, they also need to know how quickly they adapt.

MTTA fills that gap. It won't replace today's cyber metrics, but it will enhance them by measuring a capability that is becoming increasingly important as technology, AI and cyber threats continue to evolve.

It’s now MTTA time to shine.

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As global supply chains remain exposed to geopolitical and climate uncertainty, retail and consumer packaged goods (CPG) businesses face challenges of fluctuating prices and demand. In the United Kingdom, supply chain volatility is adding to uncertainty in raw material and packaging costs to impact CPG companies’ production economics.

Research has found that a huge majority of British retailers were not confident of scaling up their supply chain operations to meet the expected increase in consumer demand. In fact, 43% of retail leaders ranked supply chain issues among their top three business challenges in 2025, highlighting widespread concern over operational capacity in the face of rising demand and cost uncertainty.

Retail and CPG businesses can address these problems by modernizing fragmented and often inflexible systems to improve data visibility, analysis and automation across their supply chains.

Artificial Intelligence (AI) and Machine Learning (ML) technologies are imperative to this agenda: packing powerful capabilities - including data integration, automation, demand sensing and intelligence - AI and ML are redefining retail and CPG operations by enabling seamless, autonomous ecosystems.

Connected platform to satisfied customer

Connected supply chains provide transparency to allow organizations to manage operations and inventories in real-time to improve stock allocation and ordering efficiency.

Intelligent algorithms also analyze vast and varied data – from historical sales and seasonality to weather, social media trends and local events – to accurately forecast demand, preparing supply chains for changing ordering patterns.

Forewarned, businesses can rapidly adapt production and distribution strategies in case of a surge in demand or supply disruption. AI and ML automate a variety of supply chain tasks to speed up processes, reduce errors and save costs. Ensuring product availability at all times, retailers and CPG companies in the U.K. can look forward to greater customer satisfaction and loyalty.

Real-time, for responsiveness and resilience

Real-time insights are critical to supply chain operations. Live data from Point of Sale systems, ecommerce platforms, etc. provide up-to-date insights into customer preferences and behaviors to improve demand forecasting and also allow companies to dynamically adjust stock levels across warehouses and stores to meet sudden demand.

Real-time analytics solutions highlight product performance by region, channel and even outlet, enabling retailers to optimize assortments and launch targeted promotions. By providing continuous visibility into the supply chain, AI allows CPG companies to anticipate demand shifts and disruptions to curtail risk and maintain supply chain resilience.

Precise prediction drives product fulfilment

AI and ML platforms have superior predictive capabilities: they go beyond general demand forecasting to predict exactly what customers want, and when, and can even proactively trigger replenishment orders before stocks run out. Algorithms are not only more efficient at spotting patterns and making projections than traditional analysis, but are even capable of adjusting forecasts based on micro-trends.

By enabling CPG and retail companies to ensure that the right product is at the right place at the right time, AI and ML mitigate loss of sales due to stockout; save labor, warehousing and other costs; and improve product availability across physical and digital channels, leading to superior customer experience.

The future is autonomous

Agentic AI-powered autonomous supply chains are taking operational efficiency and customer engagement to new heights by anticipating demand, optimizing inventory, and orchestrating various tasks with little or no human intervention.

Autonomous systems meet the digital consumer’s expectation of frictionless, enjoyable experiences by personalizing product and delivery options and continuously optimizing logistics and transportation routes to allow faster/ on-time delivery.

Upon anticipating a delay, AI agents can take proactive measures – rerouting a shipment or suggesting an alternative supplier, and updating customers about the status of their orders to avoid frustration. By enabling full traceability to allow customers to track their orders any time, autonomous supply chains build trust and engagement.

Last but not least, by optimizing inventory and logistics operations, autonomous supply chains reduce waste and energy consumption, and support ethical sourcing through clear visibility. Agile, proactive, and sustainable – that is what the future of retail and CPG supply chains looks like.

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The prospect of bringing more of Britain's critical national infrastructure into public ownership has prompted plenty of debate about investment, governance and accountability.

Far less attention has been paid to what it could mean for cybersecurity.

Regardless of where you stand politically, one thing is clear. Public ownership does not make cyber risk disappear.

If anything, it raises expectations that essential services will be more resilient, more coordinated and better prepared to withstand disruption.

That expectation reflects the reality of the threat landscape. Energy providers, water companies, transport operators and healthcare organizations all sit at the center of complex digital ecosystems. Their ability to deliver essential services depends on thousands of suppliers, technology vendors and third parties.

When one organization is compromised, the effects can spread well beyond its own network. Resilience therefore depends on far more than protecting individual organizations. It depends on understanding and managing the relationships between them.

If the government is serious about strengthening national infrastructure, cybersecurity must become part of that conversation from day one. That means moving beyond isolated security programs and towards a model where organizations share intelligence, understand common risks and coordinate their response before disruption spreads.

Critical infrastructure extends beyond organizational boundaries

Some of the defining cyber attacks of recent years have demonstrated that attackers are rarely interested in a single target. They look for opportunities to compromise one organization in order to reach many others.

The SolarWinds attack remains one of the clearest examples. By compromising trusted software updates, attackers gained access to thousands of organizations around the world. More recently, the ransomware attack on Synnovis disrupted pathology services across several NHS trusts, leading to cancelled operations, delayed appointments and widespread disruption to patient care.

Neither incident remained confined to the organization that was initially compromised. Both exposed the reality that critical infrastructure now depends on interconnected supply chains as much as physical assets.

That presents a challenge for every operator of critical national infrastructure. Security can no longer be viewed solely through the lens of protecting your own estate. Organizations also need visibility into the threats affecting suppliers, partners and the wider ecosystem. A vulnerability within a software provider or outsourced service can quickly become a problem for every organization that depends on it.

This is where many existing security programs begin to show their limitations. Organizations have invested heavily in detection technologies, vulnerability management platforms and threat intelligence feeds. They are collecting more information than ever before. Yet many still struggle to translate that information into confident operational decisions.

Better decisions start with better intelligence

The cybersecurity industry has spent years focusing on visibility. The assumption has been that if organizations can discover every vulnerability, identify every asset and collect every threat feed, they will naturally become more secure.

The evidence suggests otherwise.

Filigran's recent State of Threat Management report found that organizations consume an average of fourteen different threat intelligence feeds, yet fewer than half have fully operationalized that intelligence across their security programs.

At the same time, 84% of respondents said the attacks they experience exploit risks that were already known but had not been prioritized. Almost every organization surveyed also reported difficulty determining whether identified exposures were genuinely exploitable.

Those findings illustrate a wider industry problem. The challenge is no longer discovering risk. It is deciding which risks deserve immediate attention.

Security teams are surrounded by alerts, vulnerability reports and intelligence updates. Every tool claims to identify another critical issue demanding urgent action. Without context, everything starts to look important. Analysts spend valuable time investigating vulnerabilities that may never be exploited while genuinely dangerous attack paths remain hidden among the noise.

That has consequences beyond operational efficiency. Every hour spent investigating a low priority issue is an hour that cannot be spent reducing real business risk. Organizations are not simply overwhelmed by the volume of information. They are overwhelmed by the number of decisions they are expected to make every day.

Threat intelligence should shape decisions long before an incident

One reason this happens is that threat intelligence is still too often treated as a function of the Security Operations Centre. Intelligence is gathered, analyzed and used to help detect or investigate malicious activity once attackers have already reached the network.

Yet, threat intelligence has far greater value when it informs decisions much earlier in the security lifecycle.

Used effectively, it should help organizations understand which vulnerabilities are actively being targeted, which attack paths present the greatest business risk and which remediation activities will deliver the greatest reduction in exposure. Rather than treating every vulnerability as equally urgent, security teams can focus on the threats that genuinely matter to their environment.

This is also where Continuous Threat Exposure Management, or CTEM, has an important role to play. CTEM should not be viewed as another technology category or another security acronym. It provides a structured framework for connecting threat intelligence, exposure management, validation and remediation into a continuous process. Instead of relying on assumptions or theoretical risk scores, organizations can validate whether a vulnerability is genuinely exploitable before committing time and resources to fixing it.

Perhaps the biggest obstacle is not technical at all. Many organizations still operate with threat intelligence, vulnerability management, penetration testing and governance teams working independently, each with different priorities, processes and tooling. Breaking down those silos often delivers greater improvements than introducing another security platform.

Building a national capability

If critical infrastructure is expected to become more resilient, collaboration has to become part of everyday operations rather than something that only happens during a major incident. That thinking is already beginning to take shape.

Earlier this month, the National Cyber Security Centre and GCHQ issued a call for industry, academia and critical infrastructure operators to help define Cyber Shield, a proposed national cyber defense capability designed to combine AI, shared intelligence and coordinated defense at national scale.

Significantly, the initiative recognizes that the government cannot build this capability alone. It will depend on close collaboration with the organizations responsible for protecting the UK's essential services.

Additionally, the Cyber Security and Resilience Bill provides an opportunity to strengthen that approach by encouraging greater consistency across essential sectors. Frameworks such as the National Cyber Security Centre's Cyber Assessment Framework already give organizations a common language for measuring resilience.

They become even more valuable when they encourage organizations to learn from one another instead of tackling similar challenges in isolation.

Open standards have an important role to play as well. The Dutch National Cyber Security Centre recently made STIX and TAXII 2.1 the mandatory standard for sharing cyber threat intelligence across government.

While technical on the surface, the decision reflects a broader principle. When organizations exchange intelligence using common standards, they remove friction from collaboration and can respond to threats more quickly.

Technology alone will not deliver that outcome. Artificial intelligence, automation and modern security platforms can help organizations process more information and reduce manual effort, but they still depend on good intelligence, sound governance and trusted relationships.

For the simple reason that fast decisions only become good decisions when they are supported by the right context.

Resilience is a shared responsibility

Whether more of Britain's critical national infrastructure ultimately moves into public ownership is only part of the story. Cyber attackers do not distinguish between public and private organizations. They target weak links, trusted suppliers and interconnected systems wherever they find them.

The organizations that will be best prepared for the years ahead will be those that treat resilience as a collective responsibility. They will operationalize threat intelligence before incidents occur, validate real-world risk rather than relying on assumptions, and collaborate across organizational boundaries as readily as attackers do.

Protecting critical infrastructure has never been solely about defending individual organizations. It is about strengthening the entire ecosystem that keeps essential services running. If the UK wants to build genuinely resilient national infrastructure, that is where the conversation needs to begin.

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• UK workers could unlock more than three weeks of unproductive time a year by embracing flexible working
• However many say they aren't confident in their mobile networks
• Boosting connectivity could lead to a potential £115 billion productivity boost to the UK economy

Embracing flexible working could help give the British economy a major boost, and unlock productivity across the nation, new research has claimed.

A report from VodafoneThree says boosting "connectivity confidence" to work on the go could help unlock more than three weeks of unproductive time a year, leading to a potential £115 billion productivity boost to the UK economy.

However, the report noted this all depends on being more confident in such mobile working, which can only happen if the UK's mobile networks are resilient and reliable enough.

No more wasted time

The study, which surveyed 2,000 adults across the UK, found a third of Brits feel they are unable to leave their home or office Wi-Fi to get things done on-the-go, while almost half (46%) spend most of their working day at a desk.

Nearly half (43%) of those with a job said they would prioritise working on the move if they felt more confident in the quality of the UK’s mobile networks, with accessing and collaborating on work documents, taking calls on commutes and joining remote NHS consultations from phones all named as big tame savers.

British workers said they wasted almost two hours a week being unproductive on the commute, and over an hour sitting in waiting rooms and waiting to pick up children or dependents - wasted time which totals more than 10 hours a week, more than France, Germany, Spain, Australia, Singapore, Hungary and South Korea.

The lack of flexibility even extend into the weekend, as more than a third (34%) said their weekend to do list would be shorter if they could be more productive on the go, with booking all GP appointments through apps or their phones (23%), pay utility bills (27%) and book tickets on-the-go (16%) also named as tasks which could benefit from better connectivity.

“Boosting the UK’s productivity doesn’t have to mean spending more hours behind our desks," noted Andrea Donà, Chief Network Officer at VodafoneThree. "But to change the way the nation works, we need mobile networks that keep us connected to colleagues, technology and essential services on the move."

“The UK Government is rightly taking this issue seriously. With improvements to the planning system and regulations fit for the digital economy, our networks can help to make public services more accessible, close digital divides and lay the foundations for growth.”

The survey comes as VodafoneThree looks to continue its rollout of a UK-wide £11 billion investment programme to help raise the speed and quality of connectivity across the country following its merger with Three in 2025.

The company hopes this expansion will bring 5G Standalone coverage to 99% of the UK population by 2030, and 99.96% by 2034, helping millions of people to work, shop and access public services seamlessly.

The reality for many organizations is that their employees are already being deceived. So, probably are their customers, their investors, and their board.

For years, the warnings have focused on the impact of deepfakes, such as fake CEOs on video calls, cloned voices authorizing payments, and fraudulent emails.

The tactics themselves are not new, but over the past few years, AI has made them cheaper to produce, harder to spot, and far more convincing within the ordinary flow of business.

It’s this trend that means that the principle of Zero Trust is becoming as relevant to how information is treated as it has been to access.

In cybersecurity, Zero Trust starts from a simple assumption: no user, device, application or request should be trusted by default.

In the age of AI-generated misinformation, businesses need to apply that same mindset to the information that moves throughout their organization.

The new trust crisis

Employees, customers, investors and partners are all making decisions based on what they see, read and hear. If that information is false, manipulated or stripped of context, the consequences can move quickly from confusion to commercial damage. Which is why misinformation, disinformation, and malinformation need to be treated as business risks.

Misinformation – the false content that spreads without deliberate intent – has always been an issue. Disinformation, constructed specifically to deceive, is now easier to manufacture at scale than ever before. And malinformation – true information, often deliberately stripped of context and weaponized – might be the most insidious of the three. A competitor, a criminal group, or an activist campaign no longer needs to breach a network to cause serious damage. They can influence those associated and concerned with an organization simply by shaping what those people see and believe.

What makes this particularly difficult for businesses is that it mirrors something individuals are already struggling with. In an environment saturated with AI-generated content, the habits that people must employ to protect themselves – pause before reacting, questioning the source, verifying before acting – are the same habits that organizations must build into how they operate.

Essentially, the instinct to trust has become a vulnerability. And addressing that requires something closer to a structural response than an awareness campaign.

The importance of verified trust

This is where Zero Trust becomes the strategy. Traditionally, organizations have thought about Zero Trust through the lens of least privilege, ensuring that the right users have access to the right applications, and nothing more. But in an AI-driven information environment, that principle needs to evolve. Businesses can no longer focus just on who is requesting access. They also need to interrogate what information is being used, what action is being taken, and whether the intent behind the action can be trusted.

The next stage is going beyond authentication and investigating authenticity, and asking questions such as “Is this information verified?”, “Is this image real or AI-generated?”, and “Has this content been edited?”. Zero Trust gives businesses a framework for answering those questions. It forces organizations to verify before they act, limit exposure where they can, and reduce the risk of false, manipulated, or decontextualised information moving unchecked through the business.

Standards bodies such as the C2PA (Coalition for Content Provenance and Authenticity) show the direction that this is heading: a future where provenance and integrity are embedded in digital content itself, the same way a padlock in a browser indicates that the connection is secure. Essentially trust won’t be something that businesses need to check for, rather it will be something that travels with the information as provenance feeds verifications. Every piece of content therefore becomes a signal in a continuous trust decision.

Developing Trust in the agentic era

The need to trust intent has become even more pressing as AI agents enter the workplace. These agents will increasingly operate like another person working alongside us, mirroring our behaviors, such as reading documents, interpreting data, making decisions, and acting. The difference, however, is that these non-human identities are moving at machine speed, where human-speed verification has no hope of keeping up.

That means AI agents must be governed through a Zero Trust model from the outset. An agent should not be trusted just because it sits inside the enterprise, has been approved by a user, or is connected to corporate systems. Its identity, permissions, behavior and outputs all need to be continuously validated. Just as importantly, agents should be governed by least privilege, the principle of least information, and least function, granting only the minimum access, data, and capability required for a specific task.

However, these agents create a trust challenge that identity management alone can’t solve. Businesses will need to know whether they are dealing with a human or a machine, whether an agent is behaving responsibly, and whether its actions reflect an organization’s values and boundaries. Effectively, businesses will need to adopt an operating constitution that agents are continuously measured against.

And in this AI era, enterprises must interrogate information, content, intent, behavior, and action in realtime and continuously as identity is generally just checked as front door access. However, given the scale of the task at hand, it will take AI to audit, flag, and govern as needed and keep the chain of trust intact.

Engineering trust into the business

The organizations that succeed will be those that treat trust as something to be engineered, rather than assumed. Misinformation, disinformation, malinformation are security, resilience, and leadership challenges, and AI is making them harder to ignore.

As technology continues to shape how information is created, shared, and acted upon, businesses need to build the same discipline around authenticity that they’ve always applied to access. That means verifying content, questioning intent, and limiting what AI systems can do to what they actually need to do.

Trust can no longer be the default setting. Instead, in today’s operating environment, it must be a decision that’s made continuously, at machine speed, across information, intent, behavior, and action. The good news is that the framework already exists in Zero Trust. What needs to change however is how organizations apply it, broadening the scope to include information, intent, behavior, and action.

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Artificial intelligence is entering a new phase, one defined not by experimentation, but by operational deployment in environments where the stakes are high and the margin for error is narrow.

Nowhere is this shift more visible than in critical services such as healthcare, where organizations are beginning to rely on AI not just for efficiency gains, but for decisions that directly affect lives, outcomes and public trust.

As a result, the conversation around AI capability is expanding, and there’s a real need for AI systems to be sovereign, trusted and aligned to the legal, ethical and operational frameworks of the jurisdictions they serve.

Sovereign AI is emerging as a response to this need.

It is not a marketing term or a technical preference; it is a structural requirement for organizations that operate under strict regulatory oversight and handle sensitive citizen data.

For these sectors, sovereignty is the mechanism that ensures AI systems remain under the control of the people and institutions accountable for their outcomes.

Data residency

The distinction between data residency and true sovereignty is central to this shift. Data residency simply describes where data is stored or processed. It is a geographical statement, not a legal one. Data sovereignty, by contrast, defines who controls the data, who can access it and which laws apply. It is a statement of legal authority and operational control.

Sovereign AI goes further still. A sovereign by design AI system ensures that every stage of the AI lifecycle, from training and fine tuning to inference, deployment and monitoring, sits entirely within the sovereign perimeter. This includes the IT infrastructure, the data pipelines, the model governance processes and the personnel who operate and maintain the system. Nothing crosses borders, and nothing falls under the jurisdiction of external authorities.

For critical services such as national healthcare systems, this level of assurance is not optional. These organizations must protect patient confidentiality, maintain public trust and comply with regulatory frameworks that are among the most stringent in the world. They cannot rely on AI systems whose training data is opaque, whose operational footprint spans multiple jurisdictions or whose governance structures are not aligned to local laws.

They need systems that are transparent, explainable and auditable, systems that can demonstrate not only what they do, but how and why they do it.

Regulated sectors

This is one of the reasons why organizations in regulated sectors are increasingly looking beyond general purpose AI models. These models have driven much of the recent excitement around AI, but they are not always suitable for environments where accuracy, safety and accountability are paramount.

Their training data is broad and often scraped from the open internet. Their provenance is difficult to verify. Their operational controls vary widely. And their governance frameworks are not always designed with regulatory compliance in mind. In contrast, domain specific AI models built on trusted, curated datasets offer a level of precision and contextual understanding that general purpose models struggle to match.

They can be aligned to clinical workflows, diagnostic pathways and sector specific terminology. They can be governed with the level of transparency and auditability that regulators increasingly expect. And when built within a sovereign architecture, they can operate entirely within the legal and ethical boundaries required by critical services.

The rise of sovereign AI signals a broader transformation in how regulated sectors will adopt and govern AI over the next decade. AI architectures will become more localized, with sovereign cloud regions, isolated compute environments and jurisdiction specific MLOps pipelines becoming the norm. Governance will become as important as model performance, with explainability, auditability and lifecycle control treated as first class requirements.

Regulators will demand greater transparency around model provenance, training data lineage and operational controls. And AI supply chains, from data ingestion to model deployment, will be scrutinized with the same rigor applied to other critical infrastructure.

What this future looks like

Healthcare offers a clear illustration of what this future looks like. When deployed responsibly, sovereign AI can automate clinical workflows while maintaining strict data protection, support diagnostic decision making with transparent and explainable models, improve patient flow through predictive analytics and optimize resource allocation across hospitals and care pathways.

By reducing administrative burden and helping ensure patients are directed to the most appropriate care pathway more efficiently, it also has the potential to improve productivity and support better use of constrained healthcare resources.

It can also enable population level insights without compromising privacy, allowing healthcare systems to plan more effectively and respond more rapidly to emerging challenges. These benefits are only achievable when the underlying AI systems are trusted, transparent and sovereign.

Sovereign AI represents a turning point in how critical services approach digital transformation. It acknowledges that trust, governance and domain expertise are just as important as model capability.

It recognizes that AI must be built to serve the needs, values and legal frameworks of the communities it supports. And it reflects a broader truth: as AI becomes more deeply embedded in essential services, sovereignty will not be a niche requirement. It will be the standard.

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• Schenker Element 16 (E26) is on sale now, pairing Intel Panther Lake processors with a 120 Hz 1600p display and a 72 Wh battery in a 1.9 kg chassis, shipping in late August
• The laptop's screwless quick-release chassis and a firmly planned next-generation mainboard swap make it a potential contender to other semi-modular Framework-built laptops rivals
• Its 72 GB RAM ceiling is an interesting configuration, thanks to 8 GB of soldered memory plus a single SODIMM slot that accepts a module with up to 64GB of memory

Schenker has put its long-teased Element 16 on sale worldwide, and the spec sheet leads with a number you will not find on any other laptop this year: up to 72 GB of DDR5 memory.

The 16-inch machine, sold under the model code E26 through the company's Bestware store, starts at 1,499 euros, with orders expected to ship in late August 2026.

Buyers can also get a 5% early-bird discount, available until the end of the month, plus a 20-euro discount voucher if they sign up for the store's newsletter.

A Framework competitor with an interesting RAM configuration

The Schenker Element 16's potential 72GB of RAM deserves scrutiny for a few reasons: how it is configured to get there and whether it's available. The Element 16 pairs 8 GB of soldered DDR5 with a single SODIMM slot, and filling that slot with a 64 GB module produces the magic number (72 GB) in an 8G+64G configuration.

This may not have been intentional; ComputerBase, a German news outlet, reported (DE) that Schenker originally planned a replaceable LPCAMM2 module for this design before settling on the soldered-plus-SODIMM arrangement that shipped.

The silicon is Intel's new Panther Lake generation, marketed as Core Ultra 300. Buyers choose between the eight-core Core Ultra 5 325 in the base configuration and the 16-core Core Ultra 7 356H for a 150 euro premium. Both chips include an NPU for local AI workloads, and both rely on integrated Intel graphics with four Xe3 cores rather than a discrete GPU.

By the standards of a 1.9 kg machine with all-day battery ambitions, the Element 16 is genuinely quick, and 72 GB of memory will happily swallow the virtual machines, containers, and outsized datasets that justify a configuration like this. Getting the memory configuration, however, might be easier said than done, however.

While the laptop can be configured with up to 72 GB, the configurator says the memory configuration is "currently not available." Users can still opt for a Kingston Fury or Crucial memory module that currently offers a 40 GB SKU.

If you are willing to wait, however, the Schenker Element 16 is a compelling alternative to the popular, modular Framework lineup that's been all the rage lately for its upgrade-friendly designs.

The 16-inch IPS panel runs at 2560 x 1600 with a 120 Hz refresh rate, 400 nits of brightness, full sRGB coverage, an anti-glare finish, and a 180-degree hinge. Storage sits in an M.2 slot with PCIe 5.0 connectivity, configurable up to 8 TB, which Schenker's technical FAQ notes is a first for its thin-and-light lineup, since Panther Lake supplies the PCIe 5.0 lanes directly from the processor.

The laptop also meets MIL-STD-810H durability standards and offers solid connectivity, including two Thunderbolt 4 ports with DisplayPort and Power Delivery, Wi-Fi 7, HDMI, two USB-A ports, a microSD reader, and an audio jack.

What distinguishes the Element 16 in a crowded 16-inch market is the promise behind its quick-release bottom panel: a mainstream-looking laptop whose maker has publicly committed to at least one more generation of drop-in mainboards. Whether that commitment holds, is financially viable compared with buying a new laptop, and offers multiple trims based on consumer needs remains unanswered for now, even if you are willing to wait an unstated amount of time to get it in a 72 GB trim.

• Chinese researchers built an AI targeting infrared traces from F-22 and F-35 engines
• Flare decoys reportedly fail to fool this heat-based recognition model
• Lab tests show recognition accuracy above 90 percent for simulated targets

Chinese researchers have claimed a lightweight AI system can identify heat signatures resembling those of F-22 and F-35 stealth fighters.

The F-22 and F-35 are designed to make enemy detection and tracking more difficult, particularly by reducing their visibility to radar and other sensors.

That makes any technology capable of recognizing their remaining infrared signatures potentially important for detecting aircraft built around stealth.

AI recognition focuses on aircraft heat

Unlike radar-based detection, infrared systems look for heat produced by an aircraft's engines, exhaust, and heated surfaces during flight.

The researchers claim that AI can analyze those patterns and distinguish simulated F-22 and F-35 signatures from other airborne objects.

Pilots often use flares to confuse conventional heat-seeking sensors during an engagement.

However, the researchers say that aircraft-generated heat differs from flare emissions, giving an AI system another basis for separating an aircraft from decoys.

The researchers tested their recognition model using simulated targets representing the thermal characteristics associated with F-22 and F-35 aircraft.

According to the study's lead author, the approach is intended to combine rapid processing with sufficient recognition capability for missile applications.

“Lightweight recognition models could become widely used in future air-to-air missiles because they can provide high-speed recognition while maintaining strong identification capabilities,” said An Jiangshan, first author of the study.

Laboratory testing reportedly produced recognition accuracy exceeding 90%, although operational performance against real aircraft remains unverified.

That distinction matters because real aircraft generate changing thermal patterns under different speeds, altitudes, maneuvering conditions, and environmental circumstances.

The researchers also describe the system as lightweight, suggesting that its computing requirements could be suitable for missile-mounted hardware with limited space and processing capacity.

Limits remain around real-world performance

Stealth aircraft are designed primarily to reduce radar visibility, but infrared emissions remain an important consideration for infrared-guided weapons.

Heat-seeking missiles already use infrared sensors, while machine-learning systems could potentially assist those sensors with identifying complex thermal patterns.

However, the reported research does not establish that the system can reliably track operational F-22 or F-35 aircraft under combat conditions.

Actual engagements would introduce atmospheric conditions, changing viewing angles, aircraft maneuvers, background temperatures, and electronic countermeasures that laboratory testing may not fully reproduce.

Even without proving the system can defeat operational aircraft, the research points toward a growing challenge for American stealth platforms.

The United States cannot assume that reducing radar visibility will remain sufficient as AI systems become better at recognizing infrared patterns.

Future F-22 and F-35 upgrades could therefore require greater attention to heat management, exhaust signatures and other infrared characteristics.

The same concern applies to future stealth bombers, which could face increasingly capable AI-assisted infrared sensors.

Stealth designs may need to consider how machine-learning systems interpret heat patterns rather than simply minimizing the strength of those emissions.

That could become increasingly important as lightweight AI processors become easier to integrate into missiles and other airborne weapons.

American defence planners have little reason to wait until such systems demonstrate their capabilities during actual combat.

Via SCMP

• Iran has destroyed dozens of Reapers, exposing America's costly drone vulnerability
• Forty-five Reapers lost represents roughly one-quarter of America's pre-war fleet
• Washington is buying replacement Reapers as battlefield losses continue mounting

The United States has lost roughly a quarter of its entire MQ-9 Reaper drone fleet during its war with Iran, new reports have claimed.

The Washington Post notes approximately 45 of these unmanned aircraft were destroyed in the conflict - which represents about 25% of the fleet, which numbered approximately 185 units before fighting began.

The report comes as the US slowly realizes how Iran's air defense network became the central challenge facing the Reaper throughout sustained operations near its territory.

Why Iran's defenses inflicted huge losses on the US

The MQ-9 Reaper is a remotely operated aircraft built by General Atomics for surveillance and precision strike missions - it can remain airborne for approximately 24 hours and operate at altitudes reaching around 13,000 metres.

Its weapons include AGM-114 Hellfire missiles and Mark 82 guided bombs, allowing one aircraft to conduct reconnaissance and strikes.

These drones fly at relatively low speeds and altitudes, leaving them exposed to ground-based interception systems.

Some aircraft were lost not in flight but while sitting on U.S. military bases during Iranian strikes.

Intensive Reaper use over Iran and near the Strait of Hormuz drove much of the attrition.

Replacing these losses quickly has proven difficult, since building new Reapers requires considerable time and resources.

A single MQ-9 Reaper system can cost up to $30 million, pushing total losses past $1.3 billion.

The US military must also continue supplying drones for other ongoing operations happening elsewhere simultaneously.

Because of this strain, Washington has already resorted to purchasing remaining Reapers from General Atomics' existing stockpiles.

The MQ-9 Reaper serves reconnaissance and strike roles for several allied nations.

Current operators include Belgium, the United Kingdom, Denmark, India, Italy, Spain, the Netherlands, Poland, France, and Japan.

Wider pressure on America's weapons stockpiles

These drone losses form part of a broader pattern affecting U.S. ammunition and interceptor stockpiles during the conflict.

Iran has used the 358 air-defense missiles, which have an estimated cost of $20,000 to $90,000 per missile.

Even if Iran fired 100 of these missiles at one MQ-9 Reaper, the total expenditure would reach only $2 million to $9 million.

With each Reaper costing tens of millions of dollars, such an exchange would still leave Iran with a substantial financial advantage.

Washington appears to have underestimated how quickly prolonged confrontation with Iran could consume its air-defense interceptors.

The Pentagon declined to comment specifically on Reaper losses and consistently denies that its stockpiles are running low.

Defense Secretary Pete Hegseth disputed recent reports claiming military commanders had flagged critically low interceptor stockpiles.

Regardless of that dispute, the Pentagon has pressed defense contractors to accelerate production of critical weapons systems.

Amid mounting losses, the United States has also begun searching for cheaper drones deployable in far larger numbers.

Such alternatives could reduce the financial and strategic cost of losing individual, expensive aircraft during future engagements.

Whether these cheaper alternatives can match the Reaper's endurance and weapons capacity remains an open, unresolved question.

The ideas that we take for granted in today's computing landscape were once all original thoughts that somebody had to devise and implement out of nothing. Both Apple and Microsoft were pioneering companies in this space, but not all great ideas that stuck necessarily came from either company.

Best of frenemies

When word got out that Bill Gates had announced Windows for the first time at the Comdex trade show, Steve Jobs was furious and asked for a face-to-face meeting.

According to an account from Apple's designer of Macintosh system software, Andy Hertzfeld, Jobs "went ballistic" upon learning that Microsoft's new mouse-based system had made its debut.

The two companies had agreed a staggered release – with Windows only allowed to launch in 1983, a year after Mac. Because Mac had been delayed, and that possibility was never defined in their 1981 agreement, Microsoft was still within its rights to launch Windows.

Jobs felt this was a stolen idea anyway and that it represented a betrayal of a fragile partnership. In this meeting, Hertzfeld wrote, Gates simply pointed out the fundamental idea wasn't either of theirs – and that the terms of a 1981 agreement still held up.

North star

Gates' witty quote underlines the frosty relationship that he shared with Jobs throughout their careers, until they buried the hatchet a couple of decades later.

This conflict originated when Jobs recruited Microsoft to be the first software developer for the Macintosh. Both companies, at this point, had exposure to the ideas being developed at Xerox's Palo Alto Research Center (PARC) – especially the mouse-based system the company's engineers had refined. For instance, Xerox Star was a highly influential predecessor operating system.

Microsoft didn't shift a working version of Windows until almost two years later, and, according to Hertzfeld, it was just as crude as Jobs had predicted. When Microsoft launched a version of Windows in 1988 that Jobs deemed good enough, he sued Microsoft for copyright infringement but lost on a technicality.

• A Florida company has moved from printing furniture to manufacturing military drone boats
• Haddy built its latest unmanned boat using giant robotic 3D printers
• The company completed an earlier autonomous vessel project in nine days

Haddy, a digital manufacturing company in St. Petersburg, Florida, has produced a military drone boat using large-format robotic 3D printing.

The company announced the vessel, called the TF-179 Drone Boat, but did not name the customer behind the project.

Haddy started in 2022 building custom furniture and architectural pieces before shifting much of its focus toward defense manufacturing.

A rapid build process

The St. Petersburg facility spans roughly 30,000 square feet (about 2,800 square meters) and houses eight large-format CEAD printing systems.

Seven of those machines run on rails, extending their reach to produce unusually large single-piece components without seams.

Haddy previously used this process to print a carbon-fiber-reinforced hull for HavocAI, delivering a testable vessel from design to sea trial within nine days.

"The maritime industry is redefining how vessels are designed and manufactured," Haddy said in a company statement announcing the TF-179 vessel.

Haddy released photographs of the finished TF-179 vessel alongside its statement but offered no details about the boat's intended mission.

Demand for small, expendable maritime drones has surged across the U.S. defense industry over roughly the past year, according to the company.

A shift in Naval strategy

Haddy has also partnered with Blue Ops, the maritime drone division of Red Cat Holdings, to help double production capacity for uncrewed surface vessels.

Blue Ops sells those drone boats to U.S. and allied militaries as smaller, cheaper alternatives to traditional crewed patrol vessels.

Haddy was founded by Jay Rogers, a former Marine and Princeton graduate who previously led a similar 3D-printing venture called Local Motors.

The company opened what it describes as the world's largest 3D printing facility, based on total throughput and machine count, in April 2025.

This trend toward faster, cheaper naval hardware follows direct lessons drawn from Ukraine's ongoing war against Russia in the Black Sea.

Inexpensive, remotely piloted drone boats have repeatedly damaged or destroyed far more expensive warships belonging to Russia's Black Sea Fleet.

Even in Iran, swarms of very cheap Iranian drones have been used to track expensive aircraft, some of which were eventually brought down, including a $60 million U.S. Air Force F-15E Strike Eagle and multiple $30 million MQ-9 Reaper drones.

These outcomes have pushed American and allied defense planners to prioritize speed and volume over the years-long timelines of traditional shipbuilding.

Small, disposable drone boats can be replaced and redesigned far more quickly than a single multimillion-dollar warship ever could.

Robotic additive manufacturing fits into that calculus because Haddy can adjust a vessel's design digitally and print a new version within days.

Conventional shipbuilding for crewed naval vessels can take years to complete, a pace defense planners increasingly view as too slow for today's threats.

Battlefield requirements have shifted quickly in recent naval conflicts stretching from the Black Sea to the Middle East, adding pressure on manufacturers.

Haddy has not said whether the TF-179 design will be adapted for other military customers beyond the still-unnamed client behind this particular order.

Whether this printed approach can match the durability and long-term reliability of conventionally built naval vessels remains to be seen.

Via Defence Blog

• Rheinmetall launched an armed drone from a truck while it was moving
• Each FV-014 drone carries a warhead weighing about six kilograms
• The launcher fits inside a standard 20-foot shipping container

Rheinmetall has carried out a test launch of its FV-014 loitering munition from a moving Rheinmetall HX truck fitted with its Containerized Missile Launcher.

The trial took place in July 2026 at the National Test Centre for Unmanned Aerial Systems, based at the German Aerospace Centre in Cochstedt, Saxony-Anhalt.

The company claims a single 20-foot Containerized Missile Launcher can hold up to 18 of the drones, allowing coordinated strikes against several objectives at once during a single operation.

How the drone and launcher work together

Each FV-014 weighs around 22 kilograms at launch and carries a payload of roughly six kilograms, including an integrated High-Explosive Dual-Purpose warhead.

That warhead can penetrate more than 600 millimetres of solid armour steel, according to figures released by the company.

Once ejected from its container, the drone unfolds its wings and shifts into standard aerodynamic flight for the remainder of its mission.

Its electric propeller keeps the approach quiet, while a reduced radar and infrared signature is meant to lower the odds of early detection by enemy systems.

Operators keep control throughout the flight, which can last up to 70 minutes and cover a range of roughly 100 kilometres.

This gives time for reconnaissance and identification work before any strike decision is made.

The launcher itself is built around a standard ISO container, so it can be mounted on trucks, ships, or fixed positions without major structural changes.

It connects to command and communications networks either by radio or satellite link, giving operators flexibility over where and how it is deployed.

Rheinmetall states that the system also includes a long-range power supply and a dormant activation mode, meant to keep it functional in remote locations for extended stretches without maintenance.

What the test signals for future deployment

Beyond the container launcher, the company says the FV-014 is built specifically for fast-moving, unpredictable combat conditions rather than fixed defensive lines.

Its onboard sensors and network features are designed to resist GNSS jamming, a growing concern in modern electronic warfare environments.

The system can reportedly strike armoured vehicles as well as softer infrastructure and unarmoured assets, widening its potential battlefield role considerably.

Rheinmetall also plans to extend deployment of the FV-014 to mobile land platforms beyond trucks, along with seagoing vessels.

Whether these plans move from testing toward wider battlefield adoption will depend on customer procurement decisions still pending.

The mobile-launch trial in Cochstedt appears intended to demonstrate operational flexibility to potential buyers evaluating swarm-capable loitering munitions.

Given the secrecy typical of defense procurement, further details about actual customer interest are unlikely to surface publicly in the near term.

The test represents a proof of concept rather than a confirmed operational rollout, and independent verification of the stated specifications remains limited.

• Japanese certificate authority GlobalSign revoked TLS certificates for thousands of Russian domains in June 2026 as it implemented US and EU sanctions
• As a result, seven major Russian banks now serve certificates from a state root that no mainstream browser trusts, forcing users to manually install them
• Ukraine's Foreign Intelligence Service says the losses turn Russian banking, email, and internal systems into easier targets

Russian companies are finding themselves in an extremely tricky situation as US OFAC and EU sanctions now affect international security certificates issued by third-party providers.

A statement issued by Ukraine's Foreign Intelligence Service on August 12 said the certificate withdrawals essentially left the country's internal systems, email, and banking exposed to attack because no internationally recognized certificates were issued for their domains.

It noted Russian state services, including the Federal Tax Service, increasingly fail to load in Chrome, Firefox, and Safari, and estimated that roughly 90% of the Russian market still depends on foreign-issued certificates, and that wide implementation of these sanctions could make it harder for users to access affected sites.

Web trust certificates sanctioned away?

The move began on June 13 2026 when Japanese certificate authority GlobalSign, which controls much of Russia's commercial foreign certificate market, force-revoked TLS certificates it had previously issued to Russian companies.

Its local arm said that it could not influence the parent company's decision, which came due to new CA/Browser Forum rules, starting May 4. The rules effectively made screening applicants against the US OFAC and BIS lists, and European sanctions lists, mandatory for certificate authorities rather than optional, essentially tying the certificate provider's hands.

The move was implemented in two waves; the first affected an estimated 15,000-20,000 domains, and the second affected a more specific 310 domains across 44 companies, including large domestic names such as Rosneft, Gazprombank, Alrosa, and Positive Technologies.

The Ukrainian side insists that Russian internal systems, messengers, email, and banking APIs have become "ideal targets for hacker attacks," and there is some truth to it, even if mitigation is already underway on the Russian end. It estimates that reconfiguring infrastructure could cost larger corporations as much as 10 to 50 million rubles and take up to six months, and it is an error-prone process.

The move saw Russian domain owners, including its banks and tax services, turn elsewhere, with some moving first to a Greek academic certificate authority, HARICA, before turning to China's TrustAsia, which currently provides its state entities with certificates.

A more complicated play from the Russian end is a homegrown alternative the government continues pushing: state-issued trust root certificates that users must install manually. While the Ministry of Digital Development describes manual installation of its root certificate as "safe" and as having no effect on device function, security researchers have previously warned in 2022 and again in 2026 that a state-controlled root could be abused for "HTTPS traffic interception and man-in-the-middle attacks".

Moscow's other recommendation is that users switch to Yandex Browser, which ships with the domestic root built in. Ukraine's narrative dismisses that as an alternative prone to freezing and cache failures and offering no meaningful data protection, an assessment it attributes to "experts inside Russia."

Whatever the short-term outcomes result in here for .RU domains, a move like this could further disconnect Russia from the rest of the world, with Russian services working smoothly only for users running Russian software with a state root installed.

This might already be the trajectory for a Russia reeling from cyberattacks and sanctions, but one can assume the certificate revocations will only compress the timeline to that point, whether by design or an unintended consequence.

• A Ukrainian drone strike in western Russia took out a TU-95 bomber while the craft was parked on the tarmac
• The drone technology used in the attack is based on the Chinese ZTK-150 UAV
• Ukraine’s MICH 2000s were built when China stopped selling its UAVs, adapted for use in the war with Russia

Ukrainian drones recently made a successful attack on a fleet of Russian military aircraft at an airstrip in western Russia, but while the TU-95 bomber and others were put out of action, the origins of the drones have raised eyebrows. The attack drones might have appeared to be Chinese ZTK-150s, but China has refused to sell the UAVs since 2023.

In fact, while they might look alike, the ZTK-150s have been effectively cloned by Ukraine, with customizations and improvements made to suit both the needs of the remote pilots and the demands of the Russo-Ukrainian war.

Inspired by the performance and the unavailability of the ZTK-150s, Ukraine has instead developed the similar MICH 2000s, based on information gathered during trips to China.

Critical damage

Attack drones were launched at the Engels-2 air base in western Russia on July 16, 2026, in an Unmanned Aerial Vehicle strike that targeted a Tupolev Tu-95 and four Tupolev Tu-22Ms. Aerial footage of the strike’s aftermath, assessed by the Ukrainian state security body the SBU, revealed considerable damage to the Tu-95.

“According to preliminary data, the plane suffered critical damage. It completely tore off the tail part,” says the SBU report.

How the Ukrainians achieved this has just been revealed, along with the story of how their forces managed to get their hands on drone hardware that seems extremely similar to the ZTK-150 — a drone that China has banned sales of.

The MICH 2000 is the response to a floated deal to buy some ZTK-150s from China in 2023, despite an existing restriction on exports of the hardware. So that deal switched to selling the technology itself, something the visiting Ukrainian military bosses were interested in, but was also eventually refused by the factory management. So, the intermediaries dealing with the Ukrainians took a different approach, enabling the export of eight drones to an assembly point with no export restrictions and facilitating training.

Just an outline

While a ZTK-150 costs $50,000, the Ukrainian MICH 2000 is $48,000 for the basic craft, and its resemblance to the Chinese drone is purely skin-deep.

EuromaidenPress reports that the intermediaries assisted with a certain degree of espionage at the ZTK-150 assembly line, with a source quoted: “We came there to teach pilots to fly Chinese drones. And in parallel with the training, we photographed everything that was there. Everything from the production process to the arrangement of the shelves and cabinets. They flew home with trained pilots and eight Chinese drones.”

Reports indicate an anonymous builder is assembling around 6,000 MICH 2000s a year with a possible increase to 10,000, with customizations to suit particular missions. These include warhead types, fuel tanks, electronic countermeasure protection, and even a choice of analog, digital, and Starlink communication.

MICH 2000 drones look set to lead UAV strikes against Russian targets, perhaps even breaking the conflict’s deadlock.

The Arzopa Z3FC is our top choice portable monitor for most people - whether you need a second screen for work, or a display for gaming on the move. That's exactly how our hardware editor uses this 16.1 in screen with a built-in kickstand every single day. And right now, the Arzopa Z3FC is down to $140 (was $160) at Amazon.

It's built around a 2560x1440 IPS panel with a 180Hz refresh rate over USB-C (144Hz over HDMI), FreeSync support for handheld gaming PCs and consoles, and a lightweight aluminum-alloy body that ships with a padded sleeve. In the UK, the Z3FC is currently £130 (was £160) at Amazon.

Why we recommend it

For his full 4.5-star review, our hardware editor used the Z3FC daily as a work monitor before testing its gaming chops on a Nintendo Switch, Switch 2, and Xbox Series X.

As a second screen for office work, it performed without a hitch: text stayed crisp, and switching between landscape and portrait for long documents made a real difference to our workflow. It's also light enough to carry one-handed, which matters if you're hauling it between a home office and business trips.

On the gaming side, the 180Hz refresh rate over USB-C handled everything we threw at it, and Metroid Prime 4 ran smoothly at 120Hz on the Switch 2 in performance mode. The built-in speakers surprised us too, with more depth than we expected from a display this thin.

Given he still uses this display every day for work even after the review process, there's no question this is the best portable monitor around if you need a lightweight, tablet-thin second screen.

Price Context & Historical Value

We often keep an eye out for deals on the Arzopa Z3FC, and it's cheaper now than it was when we last highlighted a price cut on this monitor, when it dropped to $150.

A $30 discount off $169.99 works out to about 18 percent, a solid cut for a 180Hz portable monitor with these specs.

Arzopa's own listed prices for the Z3FC have swung fairly widely since launch, so it's worth comparing at checkout, but $139.99 is about the cheapest we've seen for this model.

Should you buy it?

✅ Buy the Arzopa Z3FC if...

You want a lightweight second screen for business travel, split your time between work and portable gaming, or want a high-refresh-rate display for handheld consoles like the Steam Deck or Switch.

❌ Skip the Arzopa Z3FC if...

You need pro-level color accuracy (it covers 107% sRGB, not enough for demanding creative work), you want a monitor with a built-in battery, or you plan to lean on the Switch 2's HDR mode, which looked washed out in our testing.

The Catch: What to know before you buy

Color coverage tops out at 107% sRGB, which is fine for general use but not accurate enough for color-critical creative work. There's no built-in battery, so you'll need a power source to use it with consoles or mobile devices, and HDR looked notably washed out when we tested it with the Nintendo Switch 2 (turning HDR off fixed this).

• See all monitor deals at Amazon.

We ran the Samsung T9 through our full storage benchmark suite, including AJA System Test and CrystalDiskMark, and it came close to the theoretical limits of the USB 3.2 Gen 2x2 interface. And right now, the Samsung T9 1TB is down to $240 (was $288) at Amazon.

It's rated for up to 2,000MB/s read and write speeds over Gen 2x2, backed by XTS-AES 256-bit hardware encryption and drop resistance up to 3 meters onto steel plates, and includes both USB-C and USB-A cables. If you need a higher capacity, the 2TB Samsung T9 is down to $405 (was $575) at Amazon.

Why we recommend it

For most people, the Samsung T9 is our top-rated portable SSD, and has been for a long time now. If you need a speedy drive you can sling in your backpack for work or study, saving game files, or a lot of media, this is our top recommendation.

In our benchmarks and 4-star review, the T9 posted read and write speeds close to 2,000MB/s across AJA System Test, CrystalDiskMark, ATTO, and AS SSD, putting it on par with the other Gen 2x2 drives we've tested from Crucial and Kingston.

The caching system also held up well on sustained writes, which matters if you're pulling footage straight off a camera or moving large project files.

Build quality is a genuine strength: the rubberized exterior survived drop testing from up to 3 meters onto steel plates, a tougher test than most drives in this category go through, and Samsung backs it with a five-year warranty.

For more picks, see our guide to the best portable SSDs we've tested (spoiler: the T9 is ranked best overall).

Also consider

Price Context & Historical Value

A $48 discount off $287.99 works out to about 17 percent. It's worth noting we found the T9 overpriced at its original MSRP relative to competitors like the Kingston XS2000, so this discount goes a meaningful way toward closing that gap rather than being pure gravy on top of a fair price.

We imagine the price hike is largely down to the component cost increases we've seen throughout this year. For comparison, the T9 1TB model is selling for $240 at Best Buy, which is on par with Amazon. However, the 2TB model running at $440, which is more expensive.

Should you buy it?

✅ Buy the Samsung T9 if...

Your computer or camera has a USB 3.2 Gen 2x2 port, and you want to actually use that bandwidth; you move large video or photo files regularly; or you want a drive built to survive drops on set or on the road.

❌ Skip the Samsung T9 if...

Your devices only support USB 3.2 Gen 2 or Gen 1 (you won't see the speed benefit), you want IP-rated water and dust resistance, or you're on a tight budget and don't need Gen 2x2 speeds.

The Catch: What to know before you buy

The T9 only pays off if your computer, camera, or console actually has a USB 3.2 Gen 2x2 port; plug it into an older Gen 2 or Gen 1 port and you'll get the same performance as the cheaper T7. It also lacks any IP water or dust rating, unlike some rivals, and at launch we found it priced higher than comparable drives from Crucial and Kingston, so it's worth shopping around if raw price is your main concern.

Epson's EcoTank line has been the refillable-ink standard-bearer we've measured other cartridge-free printers against for years. In our tests, they excel at both document and photo printing, with the bottled ink offering more value for money compared to inkjet cartridges. But if you want to save even more, right now the Epson EcoTank ET-4800 is down to $200 (was $300) at Amazon.

This 4-in-1 printer combines print, scan, copy, and fax with Ethernet connectivity. It ships with up to two years of ink included, an automatic document feeder for multi-page scans and copies, and both wireless and Ethernet connectivity for home or small-office setups.

Why we recommend it

We've tested a range of EcoTank printers from Epson, and they've all delivered great performance for home and office use. And while we haven't gone hands-on with the entry level ET-4800, we loved the older ET-3850 and the upgraded ET-4850.

For our money, this is a great place to start if you're trying an ink tank printer for the first time, and at this price it's especially good for students preparing for back-to-school.

The EcoTank system remains the strongest case against traditional cartridge printers: Epson estimates savings of up to 90% on ink versus cartridges over the life of the printer, and the ET-4800 ships with enough ink to cover up to two years of typical use before you need to refill anything.

The ADF and fax support round it out as a genuine small-office machine rather than just a home printer, and having both wireless and Ethernet on board means you're not stuck troubleshooting Wi-Fi if your router setup is unreliable.

For more picks, including other EcoTank models, see our guides to the best home printers and the best all-in-one printers we've tested.

Price Context & Historical Value

A $100 drop from $299.99 to $199.99 is roughly 33 percent off, and it's just 99 cents more than its lowest ever price from back in August 2024. Based on that, we wouldn't expect it to drop any lower.

What we especially like here is that you get a generous amount of free ink in the box - four in total - so, the effective savings here run higher than the discount alone suggests.

Should you buy it?

✅ Buy the Epson EcoTank ET-4800 if... you print often enough that cartridge costs add up, you want fax and Ethernet alongside the usual scan and copy functions, or you're setting up a small home office and want a wired connection as a Wi-Fi backup.

❌ Skip the Epson EcoTank ET-4800 if...

You print double-sided documents constantly and want automatic duplexing (this model only supports manual duplexing), you want a unit we've already reviewed hands-on, or you don't need fax or Ethernet and would rather save money on a simpler EcoTank model.

The Catch: What to know before you buy

We haven't tested this unit ourselves, so this recommendation leans on Epson's general track record with the EcoTank line rather than hands-on verification. It's also worth knowing the ET-4800 only supports manual duplex printing, not automatic — a detail some buyers have found easy to miss, since duplexing is standard on other printers in this price range.

• See all printer deals at Amazon.

• Akira ransomware tried Safe Mode boot to disable defenses but broke its own encryptor
• Defender later flagged and quarantined payload, leaving attackers with only stolen data
• Huntress advises VPN brute‑force alerts, MFA, SIEM logging, and Safe Mode monitoring

A recent ransomware attack saw the operators Akira (figuratively) shoot themselves in the foot - and they still walked away with sensitive data, albeit limping.

Akira is a well-known ransomware group, considered one of the most active cybercriminal organizations on the internet. Its modus operandi is simple in theory: they look for an exposed VPN instance (for example, one with a default or weak password), access the domain controller, enumerate Active Directory, steal sensitive data, and deploy an encryptor.

With the encryptor they leave a ransom note, instructing the victim to reach out and negotiate a payment in exchange for the decryption key and for deleting the stolen documents and information.

However, in a recent attack, they tried to first disable the device’s antivirus and endpoint detection and response (EDR) solutions. The process backfired, resulting in the security solutions successfully spotting and quarantining the encryptor.

The good and the bad of Safe Mode with Networking

A new report published by security researchers Huntress said that after establishing persistence on a device, Akira rebooted it into Safe Mode with Networking. This Windows startup mode boots the OS with only the essential drivers and services, excluding important components such as antivirus programs or EDR agents. At the same time, it grants internet access which, for Akira, is the perfect combination.

“This means Defender real-time protection was down too,” Akira explained. “For the entire Safe Mode window, the host had no working EDR, and AV was blinded. This is MITRE ATT&CK T1688: Impair Defenses: Safe Mode Boot, a technique that ransomware families like Snatch and AvosLocker have used for years. However, this is the first time we have seen Akira use it.”

What Akira didn’t bank on was Safe Mode with Networking also preventing its encryptor from running. “Safe Mode boots with a stripped-down environment and constrained virtual memory, and the Akira process tree appears to have starved it, getting the "Out of Virtual Memory" pop-up and the cascade of PowerShell hard errors line up exactly with the moment the payload tried to kick things off.”

The operators had no other choice but to boot the device back up normally, at which point a scheduled Defender scan detected the encryptor, flagged it, and ultimately quarantined it.

“The takeaway is a little uncomfortable. While Safe Mode blinded our controls, it may also have prevented the encryption it was meant to enable. That's a lucky side effect of the attacker's own mistake in these circumstances, not a defense you can plan around,” Huntress warned, stressing that not every victim might get such a lucky break.

“Ultimately, this could be a case of winning the battle, but not the war. It's possible that a host with more physical memory or a larger page file might give akira.exe enough virtual memory to encrypt the endpoint in Safe Mode. Akira's developers or affiliates could retool the encryptor to reduce its memory demands or make its Safe Mode launch sequence more reliable, meaning that the same failure may not occur in a future intrusion.”

How to defend against Akira ransomware

To defend against Akira, Huntress recommends users set up alerts on bursts of failed VPN logins against multiple usernames from one source. It works well because Akira starts its breach with a brute-force attack against the VPN. It also says users should correlate those failures with a successful login from the same IP or ASN within a short window.

The second step is to turn on multi-factor authentication (MFA) on every VPN account. Users should also disable or IP-allowlist the SSL VPN during active attacks and, if compromised, rotate all AD and VPN credentials. “Treat everything in that Get-ADUser dump as exposed,” the researchers warn.

EDR should be deployed to every host, as well as SIEM and ingest VPN + Windows Event Logs. “The first VPN logons were visible hours before any detonation—this time advantage is only possible if the logs are on SIEM.”

Finally, users can set up alerts on boot-configuration changes and Safe Mode boots, to catch Akira red handed.

Businesses of all shapes and sizes are adopting AI to improve productivity and efficiency, but where they should be seeing improvements from this strategy, instead they’re seeing rising token costs, struggles with integrating tools, and more security risks.

As with all new technologies, adoption comes first and the procedures and governance are a few steps behind. Experiments are taking place in almost every industry, and the lessons learned will help guide other businesses into successful adoption and AI maturity.

But employees fear replacement and sometimes spend more time questioning the results of their AI assisted work. In the worst circumstances more work is created in trying to ensure employees trust the technology, and solving the new, unseen challenges that come with adapting an AI strategy.

AI costs and challenges in the road ahead

Rising token costs are one of the biggest challenges businesses face. Without clear ways to measure how token costs reflect performance, it’s very difficult to assess if AI spend is actually offering any performance benefits. This is especially true when new, more powerful models are being released - and staying ahead of the competition means the accompanying, ever-increasing costs is the price of doing business.

But while employees may have just finished their training, or setting up a new workflow for one AI model, introducing the next can increase complexity and harm any new productivity gains. There is therefore a balance to be struck between AI integration, its associated costs, and the productivity gains employees see.

Rampant spending and reckless adoption can turn an AI strategy from a business-boosting asset into a stress-inducing, trust-eroding liability.

Ruth Patterson, Managing Director, HP, UK & Ireland says that the businesses seeing the most success during this technological revolution aren’t necessarily integrating it at every turn. Instead, they’re “applying it to practical workflows in a way that protects data and delivers real results.”

I spoke to Patterson to understand the challenges businesses face in delivering an AI strategy that shows real results, and how organisations can tackle the challenges that come with adoption AI.

• How are enterprise attitudes towards AI token use changing in 2026?

Businesses are waking up to a simple but overlooked truth about AI: the more they use, the more it costs. As AI has become part of everyday work, the financial cost of millions of interactions, charged at a token level, has become much more visible – and not just to the IT department.

Finance leaders are watching token usage fill a sizeable chunk of their balance sheets, rightly prompting much closer scrutiny of where workloads are processed and whether every task needs to be sent to the cloud.

As the conversation moves from AI experimentation to value, leaders must now focus on building an AI strategy that's both commercially sustainable and operationally efficient. That means moving away from AI for AI’s sake to a more tailored approach that balances cost, security and performance.

• What are businesses prioritising as they move beyond initial AI experimentation? Where have businesses seen the greatest gains?

The biggest gains so far have come from AI taking repetitive tasks off people's desks. You will have heard this said a lot, but the benefits are real. Whether it's summarising meetings, drafting documents, searching internal knowledge or helping employees find information more quickly, AI really is giving people back time to focus on work that requires judgement and creativity. Employees increasingly recognise that the future of the workplace is AI-enabled, whereby their own skills augmented by digital solutions. To succeed, organisations must offer access to the right technology and create environments where people feel empowered to experiment with AI.

However, as more organisations begin to move beyond this experimentation phase, they are starting to become much more disciplined. They're asking whether AI is secure, whether employees are using approved tools and whether the technology is genuinely improving productivity rather than simply adding another application to the estate.

The businesses making the fastest progress in this environment aren't necessarily using the most AI. They’re the ones applying it to practical workflows in a way that protects data and delivers real results.

• What are the biggest challenges organisations face when deploying AI at scale?

Organisations must juggle several competing priorities as they scale deployment: how to protect sensitive data, manage operational costs, maintain performance and ensure employees trust the technology they're using.

The trust question is particularly important as AI moves further into everyday use. Employees need confidence that the tools are reliable and approved, while organisations need confidence that data is protected and whether the cost model is right.

Getting IT infrastructure tuned correctly is key to making this work at scale. One of the most important decisions that organisations need to make is which AI workloads belong in the cloud, and which make more sense running on the device. That is ultimately a business decision – as opposed to purely a technology or IT decision – because it affects performance, cost and security.

• How does on-device AI slot into an organisation's overall AI strategy?

I don't see cloud AI and on-device AI as competing approaches - I see them as complementary. Cloud services will remain essential for large-scale models and complex reasoning. But not every AI task needs to leave the device.

For everyday activities like summarisation, transcription or content creation, running AI locally can reduce dependence on cloud infrastructure and give organisations greater control over sensitive information. AI only creates value when it becomes part of everyday workflows. By adopting on-device AI-solutions, employees can reduce latency and increase productivity with the peace of mind that their data is secure. This offers an easy route-in for employees beginning to implement AI into their day-to-day workflows.

The right approach to enterprise AI strategy is pretty simple: place workloads where they make the most sense. Because once the right balance between cloud and local AI is found, organisations will find the sweet spot between cost, performance and security.

• How are business leaders evaluating the success of AI investments around productivity?

The conversation has moved beyond adoption metrics. Now leaders want to understand whether AI is creating measurable improvements in business performance. They're looking at time saved, faster decision-making, improvements to workflow efficiency and whether employees can spend more time on higher-value work.

Technology leaders are also beginning to examine the broader economics of AI. Productivity gains need to be considered alongside operational expenditure, infrastructure requirements and long-term scalability.

Ultimately, AI should reduce friction. If employees can complete work more efficiently while organisations maintain control over cost and governance, that's where the real return on investment begins to emerge.

AI-enabled digital solutions now offer benefits such as persona-based device optimisation or integrated sentiment analysis to measure employee satisfaction with digital tools. These innovations truly redefine experience management and facilitate higher employee satisfaction and productivity. It also enables leaders to stay closer to their employees and gauge whether AI is improving their experience or simply adding another layer of complexity.

• How do you expect enterprise AI infrastructure to evolve over the next few years?

We will continue to see significant investment in AI infrastructure. But the conversation needs to go beyond capacity, because capacity doesn’t create business value alone.

The next phase will be about ensuring infrastructure enables AI workloads to run where they are most effective. That means treating the endpoint as part of the AI infrastructure and using it to process selected tasks locally, alongside cloud services and edge computing. At the same time, we can expect to see greater emphasis put on AI infrastructure that supports governance, security and efficiency alongside raw compute capacity.

Together, these infrastructure shifts will start to deliver the practical value that employees and businesses expect from AI but aren’t currently seeing.

• What trends are you watching most closely in enterprise AI right now?

Three stand out for me:

First, the move towards hybrid AI architectures. Organisations are becoming much more deliberate about deciding which workloads should run in the cloud and which belong on the device.

Second, the growing focus on the economics of AI. As adoption scales, businesses are paying much closer attention to operational expenditure, infrastructure efficiency and the total cost of AI deployment.

Finally, we’re seeing the role of the endpoint evolve. For years, the PC has been framed as an access device only. Today, it has become an intelligent computing platform, capable of running AI workloads securely and efficiently.

Businesses don’t need a big AI budget to get all three right. They just need to spend time working out, task by task, where intelligence really belongs.

Hard drives remain one of the most affordable ways to add terabytes of storage to a PC, server, or NAS, particularly where speed isn't a priority. The 4TB Seagate IronWolf (ST4000VN006) hard drive is currently $187 (was $280) at Newegg.

This is a standard 3.5-inch SATA hard drive that can be installed in a compatible desktop, workstation, server, or enclosure. What you get over a basic desktop drive is hardware and firmware designed to cope particularly well with continuous operation and multi-drive NAS environments.

It offers CMR recording, a 5400 RPM spindle speed, 256MB cache, and SATA 6Gb/s interface. It's rated for 24/7 operation and workloads of up to 180TB per year. Seagate's AgileArray technology is designed to improve reliability and RAID performance in compatible multi-bay systems.

Why we recommend it

In his 4-star review of the 8TB model (ST8000VN004), our expert Mark said: "IronWolf drives feature CMR technology and AgileArray firmware, ensuring smooth RAID performance, reduced vibration, and efficient power management. The 180TB per year workload rating, the RV sensors, the three-year warranty, and the bundled Rescue Data Recovery Services have all persisted as defining features of the consumer tier."

Flexibility is a huge part of the HDD's appeal. Its standard 3.5-inch SATA interface means it can provide conventional storage in compatible PCs and other systems, and its NAS-specific features are particularly handy when it's running continuously or alongside other drives.

CMR recording means it's well suited to sustained writing and RAID environments, and the 180TB/year workload rating makes the drive better equipped for heavier storage duties than a typical everyday HDD.

You also get Seagate's IronWolf Health Management on supported NAS hardware, which can provide drive-health monitoring and recommendations. Seagate includes three years of Rescue Data Recovery Services as well, although availability varies by country.

For more options, take a look at our roundups of the best desktop and laptop hard drives and best NAS hard drives.

Price context & historical value

Newegg has slashed the price from $279.99 to $187, a $92.99 saving, equivalent to roughly 33%. This isn't the lowest price we've ever seen for the ST4000VN006, which has sold for considerably lower in the past (it was $169.99 in May of this year), but it's still a solid offer.

Should you buy it?

✅ Buy the Seagate IronWolf 4TB if...

You need plenty of inexpensive storage for files that don't require SSD speeds. 4TB gives you a decent room for photos, videos, backups, documents, games, and other large collections.

It's an even stronger fit if that storage finds its way into a NAS. IronWolf drives are designed specifically with always-on and multi-bay environments in mind, so you can use this as an ordinary storage drive today without ruling out a NAS or home-server role later.

❌ Skip the Seagate IronWolf 4TB if...

You need high-speed primary storage for Windows, applications, or workloads that constantly move large amounts of data. A 5400 RPM mechanical drive can't compete with an SSD for speed so its strengths are capacity, and continuous operation.

4TB might also be too small if you're filling a NAS with a limited number of bays. A four-bay system populated entirely with 4TB drives will give you just 16TB of raw capacity before accounting for RAID, so larger drives could make more sense depending on your personal needs.

The Catch: What to know before you buy

This is a 5400 RPM mechanical hard drive, so will be good to use for bulk storage, backups, media libraries, servers, and NAS duties rather than something where a speedy SSD would be a smarter choice.

If you are buying it specifically for a NAS, check the manufacturer's compatibility list for the drive before ordering. Compatibility between a particular NAS enclosure and individual drive models shouldn't be assumed.

• CVE‑2026‑65400 macOS Screen Sharing flaw exploited for cryptojacking within days of disclosure
• Attackers gained root via exposed port 5900 and deployed Monero miners using XMRig
• Apple patched in Sequoia 15.7.9, Sonoma 14.8.9, Tahoe 26.6.1; users urged to update immediately

Less than a week after being publicly disclosed, a macOS vulnerability plaguing Screen Sharing was observed as being used in cryptojacking attacks.

Alfredo Pesoli, a security researcher from Bynario, discovered an authentication issue in macOS Screen Sharing and reported it to Apple. Screen Sharing is a built-in macOS tool that allows users to remotely connect, and use, another Mac device. It is similar to third-party tools such as AnyDesk or TeamViewer and comes in rather handy for IT teams accessing Macs stored in closets or used by remote and home-working employees.

The bug allows a remote attacker to bypass authentication and gain access to a vulnerable Mac device without valid credentials. It apparently stems from a logic issue in the Screen Sharing server’s authentication process, affecting systems where the service is exposed to the internet.

The Netherlands issue a warning

Soon after disclosure, Apple released an out-of-bound fix, signaling that this is, indeed, a dangerous vulnerability. “Apple does not ship an update out of band unless something is critical,” security researchers Calif said in their technical writeup. The National Vulnerability Database (NVD) assigned it an identifier - CVE-2026-65400 - and gave it a severity rating of 9.6/10 (critical).

Approximately at the same time the patch was released, the flaw was also showcased at the 2026 Black Hat conference, with a video demonstration was made public a few days later.

Apple said it fixed it with improved state management, addressing the bug in macOS Sequoia 15.7.9, macOS Sonoma 14.8.9, macOS Tahoe 26.6.1.

Now, less than a week after the disclosure, researchers are saying the bug is being leveraged in actual cyberattacks, with Dutch security officials being first to react

“The NCSC has received a report showing that active abuse of this vulnerability has been observed on several systems on which port 5900 was accessible from the internet,” the Netherlands National Cyber Security Centrum (NCSC) said in a machine-translated report. “In all these cases, root access was gained on the affected system and a Monero crypto miner was placed.”

Why Monero?

Monero is considered an “altcoin” - a cryptocurrency built as an alternative to Bitcoin. It is one of the oldest active altcoins out there, having been launched more than 12 years ago. Most cryptocurrencies rarely live through a single four-year bitcoin cycle but Monero, just like Ethereum, Litecoin, Solana, and a handful of others, endures.

It is similar to Bitcoin because it, too, can be “mined” (unlike Ethereum, for example). It differs on the privacy front. Unlike Bitcoin, whose transactions are recorded on a public ledger and can often be traced, Monero is designed to obscure the sender, recipient, and the amount of transactions. This privacy feature has, unfortunately, also attracted criminals.

Another key feature that made crooks choose Monero for their cryptojackers is the fact that the altcoin uses a proof-of-work (mining) algorithm optimized for general-purpose CPUs, making mining relatively profitable on ordinary servers, desktops, and cloud machines.

Although it was not specifically stated, it is safe to assume that in this incident, the attackers were deploying XMRig. It is, by far, the most popular cryptojacker and one that mines primarily Monero (its ticker is XMR).

How to stay safe

The best way to go about it is to install the patch Apple just released. This effectively plugs the hole and makes the device secure. Those who are unable to deploy the patch immediately should block Screen Sharing and enable it only when it is actually needed and used. To do that, users can go to System Settings > General > Sharing and toggle the Screen Sharing switch off.

Finally, it is worth mentioning that the NCSC stressed the crooks could only exploit the flaw when the target device’s port 5900 is exposed to the internet. Therefore, setting routers and firewalls to block the port can also work, although we’d only recommend it as a last resort. Installing the patch is still the best way to go.

Right now, no groups claimed responsibility for this attack, and there is no evidence it is being used for anything else. In theory, though, it can also be used for data exfiltration, malware deployment, and possibly even ransomware attacks.

Artificial intelligence is rapidly reshaping cybersecurity. Much of the conversation has focused on how large language models are helping developers write code, but a more significant shift may be occurring elsewhere: security testing.

AI systems are becoming remarkably effective at identifying vulnerabilities, forcing organizations to rethink how they evaluate the security of both software and hardware products.

The reason lies in the nature of modern software. Large codebases are sprawling ecosystems of interconnected modules, third-party dependencies, legacy components, and undocumented assumptions.

Security flaws often emerge not from a single defective function, but from subtle interactions between components that may be separated by hundreds of files or years of development history. Human reviewers excel at deep analysis, but they are constrained by time and cognitive bandwidth.

AI systems, by contrast, can rapidly traverse vast amounts of code, correlate information across repositories, and identify patterns associated with known vulnerability classes.

Common software weaknesses

This capability is particularly powerful for common software weaknesses such as memory-safety issues, race conditions, authentication flaws, insecure API usage, and privilege-escalation paths. In many cases, AI is acting as an amplifier for established security techniques rather than inventing new ones. Yet the result is still significant: vulnerabilities that previously required substantial manual effort to uncover can now be identified at a much greater scale and speed.

The implications for product security are profound. Organizations can no longer assume that obscure vulnerabilities will remain undiscovered because finding them is too expensive. The cost of vulnerability discovery is falling, and it is falling for defenders and attackers alike. Security testing therefore becomes less about achieving a point-in-time assessment and more about maintaining continuous assurance throughout the development lifecycle.

However, it would be a mistake to assume that all areas of cybersecurity will be transformed equally by AI. Hardware security, particularly side-channel analysis of cryptographic implementations, presents a very different challenge.

Unlike general-purpose software systems, cryptographic implementations operate within a comparatively narrow and mathematically defined problem space. Side-channel attacks do not typically exploit unexpected program behavior or complex interactions between software components. Instead, they target subtle information leakage through physical phenomena such as execution timing, power consumption, or electromagnetic emissions.

The challenge is not understanding millions of lines of source code; it is extracting meaningful signals from carefully collected measurements and applying sophisticated statistical techniques to reveal hidden secrets.

Side-channel analysis

This distinction matters because many of the strengths that make large language models effective in software security are less relevant in side-channel analysis. LLMs excel at connecting information across large bodies of text and code, identifying relationships that humans may overlook.

Side-channel research, by contrast, already relies heavily on structured datasets, statistical processing, signal analysis, and deep domain expertise. AI can certainly accelerate parts of the workflow—from automating experimentation to assisting with data interpretation—but the advantage is typically more incremental than transformational.

That does not make hardware security less important. If anything, it highlights the need for specialized testing approaches. As AI lowers the barriers to software vulnerability discovery, organizations may be tempted to assume that automated tools alone can provide comprehensive assurance.

The reality is that different classes of vulnerabilities require different forms of expertise. An AI-assisted code review may uncover a memory corruption bug, but it is unlikely to replace the specialist knowledge required to evaluate whether a cryptographic implementation leaks secrets through power analysis.

The broader lesson is that security testing is becoming more important, not less. AI is increasing the speed at which vulnerabilities can be found, but it is not eliminating the need for expert analysis. Instead, it is changing where that expertise delivers the most value.

Organizations that combine AI-assisted testing with rigorous human-led security evaluation will be best positioned to address the evolving threat landscape—whether the target is a cloud application, an embedded device, or the cryptographic hardware that underpins digital trust.

We've reviewed, rated, and ranked the best internet security suites.

This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

If you're looking for a portable power station that can keep essential equipment running through an outage or provide electricity on a road trip, I've found a huge $470 discount on the Bluetti Elite 100 V2 with a 100W solar panel for $629 (was $1099) at Amazon.

The Elite 100 V2 packs a 1024Wh LiFePO4 battery into a relatively compact 25-pound unit and delivers 1800W of continuous AC output. It can power up to 11 devices simultaneously, and Bluetti rates the battery for more than 4,000 cycles. It can reach 80% charge in around 45 minutes and fully recharge from AC power in about 70 minutes.

This bundle also includes a 100W solar panel, giving you a way to boost the battery when you're camping or away from the grid. A full solar charge lasts around seven hours, under suitable conditions obviously..

Why we recommend it

In his four-star review of the Elite 100 V2, our expert Jack called it a "small, light-weight, portable yet powerful power station" and liked that it is quiet when fans are running, has a very good app and high wattage output.

The Elite 100 V2 strikes a useful balance between capacity, output, and portability. At 25 pounds and around 12.6 x 8.5 x 9.8 inches, it's reasonably manageable for a 1kWh-class power station, with a hidden handle making it easier to carry.

The UPS function can switch over in 10ms or less during a power failure, helping keep equipment such as a PC or Wi-Fi router running. Bluetti also rates operating noise as low as 30dB, which should make it less intrusive indoors.

An AC connection can take the battery to 80% in around 45 minutes and fully recharge it in roughly 70 minutes, so you don't have to leave it plugged in for most of the day before it's ready to go.

For more options, take a look at our roundup of the best portable power stations.

Price context & historical value

The Bluetti Elite 100 V2 and 100W solar panel bundle is currently reduced from $1,099 to $629, a $470 cut of around 43%.

This isn't the lowest price we've seen it however. The bundle was reduced to $550 in November 2025, for Black Friday, making the current deal $79 more expensive than that previous low.

It's still a large reduction from the listed price though, and we'd definitely recommend considering buying it now.

Should you buy it?

✅ Buy the Bluetti Elite 100 V2 if...

You want a portable power station that can cover camping trips, road trips, and emergency backup duties at home. This model's 1024Wh capacity is more than enough for charging your various devices. The 1800W continuous output also allows it to run a wide range of household equipment.

The included solar panel will make the bundle especially appealing for camping and off-grid use. You can boost the battery without access to mains power, and the panel is included rather than being an additional purchase.

❌ Skip the Bluetti Elite 100 V2 if...

You only need something to keep phones, tablets, and other small electronics charged. A 1024Wh power station has more capacity than those jobs require, and a smaller model will be cheaper and considerably lighter.

The included 100W solar panel also isn't particularly powerful compared with the capacity of the battery. Bluetti quotes around seven hours for a full solar recharge under suitable conditions, so anyone relying heavily on solar power may want a higher-output compatible panel.

The Catch: What to know before you buy

The 100W solar panel ships separately from the power station, so don't be concerned if the entire bundle doesn't arrive in a single delivery.

It's also worth distinguishing between the various power figures in the listing. The Elite 100 V2 provides 1800W of normal AC output, while Bluetti also lists higher figures for Power Lifting and surge capability. Those shouldn't be treated as the amount of continuous power available for everyday use.

• Shop generators and portable power devices at Amazon

Apple's 16-inch MacBook Pro is built for creative professionals who need considerably more power than an everyday laptop can hope to provide. And the 2026 MacBook Pro with an M5 Pro chip is currently discounted to $2760 (was $29990 at Amazon.

At a time when Apple has just increased prices across the board, that $239 is one worth jumping on. The M5 Pro chip has an 18-core CPU and 20-core GPU, backed by 24GB of unified memory and a 1TB SSD that is up to twice as fast as the previous generation. The laptop also supports Wi-Fi 7. In the UK, the M5 Pro MacBook Pro is down to £2746 (was £2999) at Amazon.

The 16.2-inch Liquid Retina XDR display remains one of the biggest reasons for creatives to choose the larger MacBook Pro. It can reach 1,600 nits of peak brightness and 1,000 nits of sustained brightness, with a claimed 1,000,000:1 contrast ratio.

Why we recommend it

The M5 Pro is the heart of the laptop and its 18-core CPU and 20-core GPU can handle even the most demanding workloads and hardware acceleration for AI which developers and creators running compatible models and tools locally will appreciate.

The 24GB of unified memory and 1TB SSD make it even more useful and the 16.2-inch Liquid Retina XDR display is beautiful with 1,600 nits peak brightness. Combined with the performance available whether you're plugged in or running from the battery, this is essentially a high-end desktop-class workstation you can carry with you.

Price context & historical value

Amazon has dropped this 16-inch M5 Pro MacBook Pro from $2,999 to $2,759.99, taking $239 (and a cent) off the regular price, or around 8%.

That's not an enormous percentage discount, but $239 is a solid reduction on a brand new high-end Apple laptop. It isn't the cheapest it's ever been however. That was $2,430 back in June, which was before Apple increased prices across its entire tech range.

Should you buy it?

✅ Buy the 16-inch MacBook Pro if...

You need a laptop for creative workloads that can actually take advantage of the M5 Pro's considerable processing power. The combination of an 18-core CPU, 20-core GPU, and 24GB of unified memory gives you a machine that can handle pretty much everything you might want to throw at it.

The large 16.2-inch XDR display gives you considerably more working space than Apple's smaller laptops, and its high brightness and contrast make it particularly attractive for photo and video work.

❌ Skip the 16-inch MacBook Pro if...

If your workload doesn't require anywhere near as much creative power then a MacBook Air or less powerful MacBook Pro with the general M5 chip would make considerably more financial sense.

Portability is another consideration. The 16-inch MacBook Pro gives you a large workspace, but if you're constantly carrying your laptop between meetings, classes, or while traveling, one of Apple's smaller models will be far easier to live with.

The Catch: What to know before you buy

Even with the discount, you're spending more than $2,700 on a laptop, so it only really makes sense if you'll benefit from the M5 Pro's performance, large XDR display, or professional capabilities. This is a powerhouse that people with advanced creative needs will benefit from, other professionals less so.

Rising demand and environmental concerns are driving governments around the world to secure resilient and scalable energy supplies.

The transition to alternative, sustainable solutions is accelerating as the limitations of traditional energy sources become increasingly apparent.

At the same time, net-zero commitments, regulatory pressures, and growing public demand for climate-conscious policies are driving an unprecedented surge in offshore wind development.

This boom in demand is in turn driving a significant shift in what the offshore workforce looks like.

As companies strive to attract the next generation of talent, offshore roles are increasingly moving onshore, creating new opportunities and transforming industry expectations.

More flexible careers

Traditionally, marine engineering careers often meant tough, hands-on work out at sea, away from home and frequently in challenging weather conditions. Today, advances in technology and connectivity are transforming those roles.

Many professionals now divide their time between offshore assignments, remote operations centers (ROCs) and office-based work, creating more flexibility and opening up new career opportunities.

ROCs are locations from which uncrewed surface vessels (USVs), ocean-going ships which can operate without a human onboard, are controlled through a satellite connection.

They are increasingly deployed for complex offshore work including inspection and maintenance, remote mapping of the seafloor, or environmental monitoring of water quality, marine habitats or other ecological data.

USVs can be used for detailed subsea inspections, ensuring the integrity and safety of underwater infrastructure, or precisely map the seafloor of a proposed location for an offshore wind farm to determine suitability.

The flexibility provided by remote operations unlocks a wider talent pool, creating career opportunities for individuals who may have personal or medical constraints that prevent them from working offshore.

At the same time, automation is improving workforce safety by reducing exposure to offshore hazards and lowering accident risk, while allowing more work to be carried out from shore-based environments.

Rich datasets

More broadly, the infusion of new technologies into the sector means the boundaries between IT and engineering are blurring as client demand for near real-time data on ocean conditions, seabed characteristics and weather patterns continues to grow.

That data can range from bathymetry and water-column data to acoustic backscatter, to understand the characteristics of the seabed’s topography and geology across a given site.

These datasets are essential to optimizing the installation and operation of offshore assets, including offshore wind farm infrastructure. Providers are under pressure to speed up the acquisition, analysis and delivery of these datasets, which can often be huge in scale, making it essential to balance the task of data management and sharing with seeking efficiencies in data analysis wherever possible.

Cloud data talent

To that end, talent is needed to help build cloud data pipelines, data lakes and the associated workflows back at base, drawing on skills in everything from data science and software engineering to project management and user experience design.

The USVs themselves accelerate the delivery of Geo-data, enabling faster access to results and recommendations so client project teams can make informed decisions sooner, compressing project timelines, reducing overall campaign costs, and cutting the time to financial return.

For the data scientists themselves, the remote nature of their role means they may be working on projects taking place anywhere in the world during their working week. From analysing near real-time data from an offshore project in the Middle East in the morning to building visualizations and client reports for projects in the North Sea later in the day.

Because ROCs are networked across multiple time zones, work can be handed over seamlessly between teams around the clock, helping to deliver insights and outcomes more quickly.

Remote responsibilities

One useful way of seeing remote operations is as a spectrum. Over time, operators may eventually oversee multiple vessels and project outcomes simultaneously, gradually shifting from direct control towards more of a monitoring and intervention role.

Importantly, remote operations do not mean the removal of responsibility. International maritime guidance already requires that every remotely operated vessel have a designated operator responsible for it, and that the operator can intervene when required.

The success of the offshore wind and broader marine engineering industry depends on rethinking traditional workforce models – bringing on board new talent skilled in data science, software engineering, and AI - as well as ensuring long-term sustainability in recruitment and talent retention.

Strategically embracing these changes will allow the marine and Geo-data industry to position itself for long-term success.

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• An Incogni survey has found people are becoming more frustrated with the internet
• Users cite AI slop and data leaks as the main reasons for stress and anxiety when using the internet
• Many users want to spend less time online, and are deleting social media profiles and messaging

For years, personal data collection for advertising, tracking, and service improvement was thought to be the fair price to pay to access the world wide web. But now, many people believe that using the internet will lead to their data being leaked or exposed.

A new Incogni survey found more than half of internet users believe their personal data will inevitably be exposed, with 63% stating that this fear causes anxiety when using the internet.

But beyond this, internet users now fear they can no longer tell what is real content uploaded by a human and what is AI generated, not only eroding trust in the internet, but also making people not want to use the internet at all. Almost half of internet users are less sure of what is real on the internet.

Inevitable data exposure

You’ve likely been prompted thousands of times to accept or reject cookies, or review a privacy policy before using a website. Every cookie you accept will collect data on your browsing habits and behavior on sites in order to display personalized adverts that you are more likely to click. Cookies also help keep you logged in and store your information on websites.

While this helps make many websites across the web free to use for those accessing them, sometimes your data is sold to advertisers or third-parties where it is stored insecurely, and can be stolen or leaked.

Among the 1,000 internet users surveyed, the fear of data exposure was strongest among the Millennials and Gen X generations. 56% of Gen X and 55% of Millennials believed that their data would at some point be breached or exposed, showing just how normal data breaches and exposure have become online.

Just 11% of those surveyed believed there was little likelihood of their data being breached.

AI is making the internet less real

AI content creation has exploded in recent years. Social media sites are full of AI ‘creators’ whose pages either share AI generated content or scrape the internet for content that users are likely to interact with, and share it through their own pages. These profiles require little human input, but can share content at an industrial scale and reap huge rewards from advertisers.

If you’ve been on Instagram lately, you may have seen videos from AI creators all sharing the same captions. “Tonight, V stepped into the crowd..” or “Japan is transforming footsteps into electricity.” These captions use keywords to abuse Instagram’s algorithm on popular topics to drive engagement, regardless of whether the content actually has anything to do with the caption.

But they’re also making it harder to know what is real on the internet.

48% of those surveyed said that they are less sure of what’s real, with 40% also stating that the lack of accountability for deepfake creators was a bother.

Additionally, 27% said that AI generated content made them want to spend less time online, with 16% also saying that navigating an internet full of low quality AI content made them feel more tired or fatigued. Just 8% of respondents said that AI generated content improved their online experience.

There are some positive attitudes to AI content online. 12% said that AI-generated content made information more accessible, with slightly less (11%) saying that they were excited about the creative possibilities AI offers.

Time to go offline?

Attitudes to how long people spend online are also changing. Over half (51%) of Gen Z internet users believe they spend too much time on consuming content on the internet, with 43% of Millennials and 42% of Gen X respondents having a likeminded view.

Whether it’s responding to emails, navigating networking platforms, finding new furniture, or looking for where to go to eat - the internet is now a life requirement for most people. And every time you access another website in work or your personal life, there is more data that could be leaked.

The ultimate effect of this is that people are being driven away from using the internet. 58% of respondents said that they had deleted a social media account or messaging app because of stress, anxiety, or privacy concerns.

“It seems that the costs of engaging with these platforms are starting to outweigh any perceived or actual benefits,” the Incogni survey said.

Or time to pay for privacy?

Incogni also asked if users would be willing to pay for an internet where tracking and algorithms did not exist. 30% of respondents said they would, but this largely relied on income. Those with a higher income were more likely to pay for this ‘private’ internet, while those with a lower income were less likely.

For many internet users, privacy shouldn’t be a luxury, but a guarantee. There is a level of trust involved when sharing personal data with advertisers, and the expectation is that the data won’t be leaked or stolen. Unfortunately, the opinions show that this is far from what's expected.

• Study finds remote workers are far happier and more productive
• Remote workers reported much higher well-being, but were also less likely to leave their role
• Study also dispels myth that remote workers might feel alienated from co-workers

Letting employees work remotely can have a significant positive effect on their productivity, job satisfaction and mental health, another major study has found.

A survey of 7,704 employees at a large US healthcare organization by researchers at the Leeds School of Business found fully remote workers reported the highest levels of well-being, with those entirely office-based reporting the lowest level.

The findings, published in Frontiers in Psychology, also noted that it was unable to discover significant evidence that remote workers felt any less connected to colleagues or workplace culture, once again disproving the myth against flexible working.

Stay flexible

"This suggests you let people work remotely if they want to work remotely," said Stefanie Johnson, professor of organizational leadership and information analytics at the Leeds School of Business and co-author of the study. "Taking away people's choice of how they work is probably not going to help them in terms of their well-being."

The researchers also looked at the level of employee turnover one year after the survey was completed, finding employees with higher well-being were less likely to leave the organization.

Remote work was associated with higher well-being, which in turn was associated with lower turnover, showing that work location cannot be a strong direct predictor of turnover.

"The data from our study and others suggest remote and hybrid work result in better outcomes than return-to-office mandates," Johnson added. "Leaders are not making decisions based on data. I think they are just returning to what they are used to."

Remote working has been a long-standing debate ever since the Covid-19 pandemic forced people around the world to be flexible.

While office-based working can have some benefits, it is increasingly noticeable that it is largely the CEOs and owners of large businesses who back a return - not those who actually have to do it, and those paying large sums of money for travel and food costs.

Amazon, Dell and Tesla are among the companies which have adopted five-day mandates, while others have also adopted four-day and three-day mandates.

Slop. The word that people have settled on, and it fits.

LinkedIn posts that say nothing. Investor decks assembled from prompts. Sales outreach that feels personalized but went to thousands of people. Conference presentations that look polished but collapse when someone asks a substantive question.

Nobody in a position of power seems to want to say this clearly, so here it is: we are making it ourselves.

People racing to scale content with AI tools are burning through the one thing that makes any of this worth doing, which is the reasonable expectation that when a person puts their name on something, a person thought about it.

When the thinking did not happen, the name is borrowed credibility, and audiences tend to feel the difference.

A denial of service attack on professional attention

Think about what high-volume, low-signal communication actually does to the people on the receiving end. It is a denial of service attack. Done at scale, they do not just waste time. They degrade the channel for everyone trying to use it honestly.

Trust in professional life is not a soft value. It is the mechanism by which work gets done. Slop corrodes that mechanism slowly, quietly reshaping expectations until the default posture toward professional content shifts from curiosity to suspicion.

The deck nobody built

Anyone who spent serious time at an event this year noticed it. You sit in the room and thirty minutes in you realize the presenter has not actually field-tested any of what is on the screen. The model made it coherent. Coherence is not the same as true.

The tell is always the same. Someone asks a follow-up question that goes one layer below the framework and the answer drifts. Not because the presenter is unintelligent. Because there was no thinking to retrieve.

This matters because a presentation is a claim: I know something about this, I worked it out, and it is worth your time. An AI-assembled deck without the underlying experience is not a weak presentation. It is a lie. It borrows the authority of hard-won perspective without having won it.

The prototype that is not a product

For most of software's history, the gap between a prototype and a product was real and substantial. Building a finished product required engineering time, design iteration, infrastructure decisions – all the unglamorous work that separates a demo from production. That gap created useful friction.

AI has compressed that gap in ways that are genuinely exciting and genuinely dangerous. You can now generate a working interface, complete with plausible data and coherent navigation, in a matter of hours. Teams can test market appetite and validate direction before committing to a full build. That is legitimately valuable.

What is not legitimate is presenting that prototype as though the hard questions have already been answered. Can it scale? What happens at the edges? What does it cost to run? These are not details to resolve later. They are the product.

A prototype shown honestly invites conversation. A prototype shown as a finished product forecloses it, and the interest on that debt gets paid in credibility.

Thinking versus the appearance of thinking

The fundamental choice we make every day, often without recognizing it is this: are we using AI to think better or to avoid thinking?

There is a version of AI-assisted work where the human is still the author. The thinking happened. The judgment was applied. Then there is the version that produces slop. The deck assembled via prompts. The prototype shown without the word prototype appearing anywhere. No thinking happened. No perspective was shared. An audience was addressed but did not receive anything.

Getting ahead of the trust crisis means being honest about which version we are practicing and having the discipline to stop calling the second one a strategy.

The bill has started to arrive. Response rates to AI-drafted cold outreach have collapsed. Investors reviewing AI-generated pitch decks are getting quicker at identifying when the strategic narrative was assembled rather than discovered. In product, the reckoning takes the form of churn.

The cruelest part is that the organizations doing the flooding harm not only themselves but everyone trying to communicate honestly in the same space. Individual rationality producing collective ruin, one generated paragraph at a time.

There is a longer consequence that rarely gets named. Frontier AI models were trained on the accumulated output of human knowledge-sharing: forums, papers, articles, the slow sediment of people working things out in public. New questions posted on Stack Overflow are down almost 80% year over year. If the knowledge commons are replaced by AI-generated output, we are not building on a base of human insight. We are iterating on a fixed one.

Slop does not just erode trust in the present. It risks stagnating the knowledge base that the future depends on.

What we owe the people we are trying to reach

As AI drives content toward abundance, credibility becomes the only real differentiator, and it is the scarcest resource in the communication ecosystem. The organizations and individuals who protect it now will hold something that becomes more valuable as the surrounding environment degrades.

Worth the time to actually write the thing, work through the strategy, build the product before calling it one. Worth asking whether what you are about to put in front of people reflects your thinking or just a model's prediction of your thinking. And sometimes it is worth saying nothing rather than saying the algorithmically optimal version of nothing.

Trust has always been the currency of professional influence. Right now, a lot of people are flooding the market with counterfeits. The people on the receiving end know. They may not say so in the meeting, or click unsubscribe, or walk out of the session. But something shifts. The next email gets opened a little more slowly. The next deck gets a little less benefit of the doubt. The next demo gets a harder question in the room.

Credibility does not collapse all at once. It drains, quietly, one hollow interaction at a time. And by the time you notice the account is empty, the withdrawals have been happening for a long time.

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• Microsoft Excel is pulling the COPILOT () function
• Function allowed users to describe tasks in natural language, including as part of a formula
• It never reached public launch, and has now been overtaken by other tools

Microsoft is dropping its COPILOT function in Excel as part of its wide-ranging overhauls to the AI platform.

In a Message Center alert, the company warned users that the tool will be pulled on September 14, 2026.

First announced in August 2025 and originally set for launch in January 2027, the tool has been in testing for over a year with Frontier and Insider users, but Microsoft has apparently decided not to move forward with its service.

Copilot dismissed

At launch, Microsoft had described the COPILOT () function as helping users to describe tasks in natural language, including as part of a formula, before receiving the results created by Copilot, in their cells, without needing to open up a separate Copilot window

It proved an initial hit for tasks such as text summarization, classification, content generation, and looking up information on the Internet.

Some examples of =COPILOT's use cases include summarizing customer feedback, categorizing data, integrating external knowledge and formatting.

In its most simple form, a function might look like "=COPILOT(prompt_part1, [context1])" – though context is optional.

Microsoft did say the function should not be used for numerical calculations, which might have been a major use cases for many - as well as saying users should avoid using it for gathering and finding information already stored inside a workbook or for content subject due to legal and compliance requirements.

So what's next? Microsoft explained that it is removing the COPILOT() function as it believes Excel cam already provide other, more effective ways to access the same AI capabilities.

Users can continue working with Copilot through both the Copilot side panel and the floating Dynamic Action Button (DAB), making a dedicated function slightly redundant.

The move comes as Microsoft makes a number of changes to AI capabilities within Excel as user demands continue to evolve and change.

This includes a new update to Copilot in Excel aimed specifically at finance workers, positioning the spreadsheet software as an AI-powered tool for financial modelling, forecasting and reporting.

Via Neowin

AI conversations have moved past the point of curiosity.

Boards and leadership teams are no longer asking what AI might eventually do.

They are asking where it is actually working, what measurable value it is creating - and why so many promising experiments still fail to become durable operating advantages.

Across industries, companies have invested heavily in AI pilots, proofs of concept and impressive demos.

Yet many remain stuck in what I think of as pilot purgatory: the place where a tool works in a controlled environment but never survives contact with the complexity, exceptions and accountability required in production.

The problem usually isn’t the model

In my experience, AI initiatives rarely fail because the underlying technology is not powerful enough. They fail because of how the technology is applied. A model can be impressive in a sandbox and still be irrelevant to the business if it is not embedded into a real workflow, connected to the right data, governed appropriately and measured against outcomes that matter.

That is why access to AI is no longer a differentiator. Anyone can buy access to models or integrate a third-party tool. The real advantage lies in the things that can’t be bought off the shelf: proprietary data, deep domain expertise, and the discipline to continuously improve AI once it is operating at scale.

For us, those principles come together in our Lean AI approach, rooted in a Lean operating model that drives continuous improvement through testing, learning, and acting. Instead of chasing technology for technology’s sake, our Lean AI approach helps us move AI beyond experimentation and into production, where it can improve service, boost productivity and create real business value.

Production AI requires discipline, not experimentation for its own sake

This is where many organizations get stuck. They treat AI as a portfolio of experiments instead of an operating capability. Organizations that successfully operationalize AI tend to do the opposite.

They prioritize AI opportunities based on business value and points of operational friction, identifying manual, repetitive and high-volume work. Then, they build and deploy agents where automation can improve speed, accuracy, scalability or service quality across the entire customer workflow.

There is no hobby AI in this model. Every deployment needs a clear business case, a workflow owner, measurement, feedback loops, and a plan to scale. That discipline is especially important with agentic AI, because agents operate with more autonomy than traditional software.

Progress is not always linear. Systems improve, encounter new edge cases, retrench and improve again. Human oversight isn’t a temporary bridge either; it is part of the architecture.

The application layer is where the moat gets built

The AI ecosystem is often described in layers, from the underlying IT infrastructure and large language models to the applications built on top of them. Those foundational layers are essential, but they are not where most enterprises will build durable, competitive moats.

The real advantage comes at the application layer—where AI is integrated into workflows, systems, exceptions, data and human judgment that define how a business actually runs. If an enterprise does not own or deeply control that layer, it risks turning AI into another generic capability rather than a competitive advantage.

Take supply chain logistics as one example. Moving a single shipment isn’t a linear task. It may require coordinating moves by truck and ship and rail, customs documentation in multiple countries, handoffs at multiple facilities, and weather and market conditions that change by the hour. A generic AI tool does not understand that workflow out of the box.

Context is the hard part

Every industry has its own data and context that powers it. In supply chains, that context lives in historical pricing patterns, warehouse operations, customer-specific policies, shipment characteristics, driver performance, market cycles and the judgment of people who have solved messy freight problems for years.

That context cannot simply be purchased. It has to be collected, structured, governed and applied. AI becomes more effective when it’s built into your technology platform and can learn from those realities rather than relying on generic information alone. Just as important, employees add institutional knowledge through continuous feedback, teaching AI agents the same way they would train a new operations employee.

Take something as seemingly straightforward as scheduling a truck to pick up freight. On the surface, it sounds like a narrow task. In practice, it requires understanding customer requirements, freight characteristics, facility policies, loading dock constraints, appointment systems and exceptions that may vary by location. An AI agent can only automate that work reliably if it has been engineered with the right context and oversight.

The companies that get the most from AI will be the ones that move beyond pilots and treat it as an operating model. That means starting with real business problems, owning the application layer where differentiation happens, feeding agents with proprietary context, keeping humans in the loop and measuring outcomes relentlessly.

AI will not reward the companies with the most demos. It will reward those that can operationalize learning faster than their competitors.

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AI spend made headlines again recently with the Claude Fable 5 model from Anthropic. Before security concerns led to the model being suspended, there were also cost concerns. Anthropic says Fable costs $10 or approximately €9 per million input tokens and $50 per million output tokens. This is double the price of the company’s previously most expensive model, Claude Opus 4.8.

Posts soon began to pop up on LinkedIn, showing just how quickly teams were going through their tokens and, as a result, their budget. There are some caveats here. Namely, that Fable 5 is an advanced model and, for most businesses, won’t need to run non-stop or be used for every task.

But therein lies a key issue: AI use is accelerating and models are evolving. But the level of control and visibility businesses have over how much is being spent, by who and for what is lagging behind.

How AI consumption became a finance problem

There is a massive shift within the UK software market toward AI and specifically Anthropic’s ecosystem. Proprietary data from Pleo looking at the top tech merchants based on number of spending customers, shows that Anthropic (Claude) surged from 12th place in Q4 2025 to 7th in Q1 2026. Meanwhile, the average spend per customer increased +43.0% in this time.

This rapid climb signals that Anthropic has reached enterprise maturity in the UK market with businesses moving beyond the experimentation phase. But while this reflects growing confidence in AI adoption, it also presents some financial challenges.

On the whole, AI has redefined how the workplace runs, but it is not a free trial. The cost of tokens has gone up, and new models that can achieve what was seemingly unthinkable a few years ago come with a price tag to match. The new challenge for business leaders is to leverage these technologies but also limit rampant spending.

This is why many organizations are turning to their finance teams. Finance has the visibility to dig into the details and map AI use across the organization, whether it quietly shows up as a subscription renewal or a new budget request. But more than that, they can be instrumental in ensuring teams embrace open conversations, not just OpenAI.

AI activity does not translate to AI value

Just about every organization will have developed transformational ways of using AI tools. But, whether they know it or not, there will be wasteful ones too.

When it comes to inefficient use, some of the major culprits include asking AI agents open-ended questions, model mismatch where tokens are burned unnecessarily; and duplicate tools, resulting from shadow AI and overlapping subscriptions. These prevent businesses from seeing the full picture; one that is, in all probability, very expensive.

User literacy can improve this. But for finance teams they must start with the grey area of AI consumption. Two teams might show as active AI users, but one that’s using an LLM to produce more content faster is doing something fundamentally different to one that’s using it for peripheral productivity tasks. In fact, only 29% of European SMEs using Gen AI are doing so in core business activities.

To improve the control they have over AI, organizations must start by elevating their visibility from who is using AI, to who is using it to become smarter, faster and more productive.

How to regain control over AI use

A complete view of AI spend is essential, regardless of whether costs are rising.

Breaking spend down by department, team and budget helps identify both disproportionate usage and areas where adoption may be lagging. These should be combined with performance metrics such as the time-to-first-draft on marketing content; code review cycle times in engineering; support ticket resolution time in customer support; and so on.

This combination of spend and performance can reveal whether AI investment is translating into measurable productivity gains and not just higher software costs.

Visibility should also extend to model-level usage. As mentioned before, the cost difference between frontier reasoning models and lighter alternatives can be tenfold. Monitoring model and vendor usage alongside token consumption helps organizations route routine tasks to lower-cost options, maximize ROI and reduce unnecessary spend.

Finance teams should therefore expand reporting and budgeting frameworks to include AI-specific metrics. A key question at month-end is whether AI-enabled teams are increasing output and capacity without increasing headcount. This provides a clear headline for AI's impact, can justify investment and distinguish between high-value and low-value AI usage.

Ultimately, effective control over AI is not about costs alone. It is about understanding where AI is creating value and ensuring investment is aligned with business outcomes.

AI control is at your fingertips

The good news is that none of these metrics require a sophisticated AI analytics stack. Finance teams should already have the tools for real-time visibility into what’s being spent and where. All that’s needed now is to fold AI into the mix and collaborate with other departments to measure and improve its ROI.

The outcome is that organizations control AI use through oversight, without restricting spend, adoption or innovation through lengthy procurement processes. Spend policies, category controls and clear approval thresholds control what is spent, and everything is measured.

But crucially, teams don’t slow down as a result. The only difference is that AI is optimized for impact.

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The legal sector’s use of AI is maturing, and its use is going beyond simply being an external chatbot or standalone tool. The focus is shifting to embedding AI into everyday legal work, giving it access to the documents, matters, knowledge and systems lawyers use every day.

As firms increase their investment in AI technology, they need to look beyond what AI models can generate and focus on a more practical question: how do those models connect to the information lawyers rely on, where does that information sit, and how is access governed?

That connection challenge is why Model Context Protocol (MCP) has started to attract so much attention.

Separating the standard from the assumptions

MCP is an open standard that gives AI tools a more consistent way to connect to external systems, data sources and applications. For all the excitement around MCP, firms need to be clear about what it does, and just as importantly, what it does not do. Otherwise, there is a risk that firms either overestimate what MCP can solve on its own or dismiss it as just another technical acronym.

The reality sits somewhere in the middle. MCP can help AI tools connect to the systems and data sources firms already use, but it does not automatically solve challenges around governance, integration, permissions or legal context. With misconceptions starting to spread across the legal sector, here are five common myths to clear up.

Myth 1: MCP is only for Claude

Although MCP was created by Anthropic, it is not just a Claude feature. It is an open-source framework and is now becoming part of the broader conversation around how AI agents connect to external tools, data sources and systems.

That distinction matters for law firms and legal organizations generally. If MCP is treated as a single-vendor feature, it can be dismissed as something tied to one model or product roadmap. But as a broader connectivity standard, firms need to think about how it fits into their wider AI strategy, integration architecture and governance model.

Myth 2: MCP replaces APIs

APIs still matter. They remain the backbone of platform-to-platform connections, allowing software systems to exchange data and trigger actions. MCP does something different: it acts as a protocol layer on top of APIs, giving AI agents a more standard way to discover and interact with approved legal systems, tools and data sources.

Put simply, MCP is closer to a universal adapter for AI, than an alternative to APIs. Just as USB gives different devices a common way to connect, MCP gives AI tools a more consistent way to understand what systems and functions are available to them, and how they can interact with those systems.

But it does not remove the need for APIs, authentication, system owners or clear rules about what AI tools can and cannot access.

Myth 3: MCP means moving documents into AI tools

There is a common misconception that connecting AI to legal systems means copying a large volume of documents into external AI platforms. In practice, AI should only be given controlled access to governed systems. Documents, precedents and matter files can remain within the firm’s trusted environment, with AI tools using MCP to retrieve only the information they are authorized to access.

Rather than creating another uncontrolled copy of sensitive material, firms can let AI work with the right information while existing permissions, security policies and governance controls remain intact. To hit the right balance, legal IT leaders should be asking “Where does the data stay, what is exposed, and how is access controlled?

Myth 4: All MCP integrations are the same

As MCP becomes more common, there will be a temptation to treat any MCP-compatible integration as broadly equivalent. That would be a mistake. MCP standardizes the connection, rather than the value of what comes through it. One integration may provide a basic route to retrieve files.

Another may provide richer information about permissions, matter relationships, document history, metadata and audit trails. Both may be MCP-compatible, but they will not deliver the same outcome.

Law firms need to focus on what the AI receives, whether access rights are enforced and whether activity can be audited.

Myth 5: MCP automatically gives AI legal context

MCP creates the route into systems, but it does not decide what the AI receives or understands. Connection does not mean context. In legal, this is more than a simple retrieval problem. An AI tool does not just need access to a document in a DMS. It needs to understand the matter, client, permissions, version history, related work and institutional knowledge around it.

For example, an AI tool may be able to find a precedent agreement, but does it know whether that precedent is current, whether it belongs to a similar matter, whether it reflects the firm’s preferred position, or whether the lawyer has permission to access the related material? Without that context, AI may generate an answer, but the answer may not be reliable enough for legal work.

That is why MCP should be seen as the access layer, not the intelligence layer. It can help AI tools connect to legal systems, but the value comes from what those systems expose through MCP: governed, matter-aware and permission-sensitive context.

Connection is only half the story

MCP gives law firms a more standard way to connect AI tools to the systems they already use. But legal AI depends on more than connection. The firms that benefit most will be those that treat MCP as the starting point, not the destination. The quality of the information, the controls around it and the context that gives it meaning will determine whether connected AI makes a genuine impact in legal work.

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Enterprise AI strategy spent two years chasing a single objective: reach the frontier before competitors do.

The default path was a public cloud account, an API key from OpenAI or Anthropic, and a willingness to absorb cost in exchange for speed.

That reality is now running out of road.

The numbers tell the story. Gartner forecasts worldwide AI spending reaching $2.52 trillion in 2026, up 44% year on year, with $1.37 trillion of that flowing into AI infrastructure alone.

In fact, in mid-2025, they claimed that procurement of AI had entered a “Trough of Disillusionment,” where scaling depends on predictable ROI, rather than visionary pilots.

The pressure has now shifted from how fast enterprises can pilot AI to whether they can sustain, govern, and defend it in production.

The Race to the Front is Over – Now Comes the Bill

We are now past AI 1.0, where simple access to cutting-edge AI was the differentiator. Now it’s AI 2.0’s turn, where inference economics, data gravity, latency and control decide the outcomes. Token prices have fallen almost tenfold annually since 2021, but AI spend overall by organizations has increased. That’s because more capable models have enabled greater ambition.

Anthropic, OpenAI, and Mistral are now stratifying offerings between flagship reasoners and lower-cost workhorses precisely because customers refuse to pay flagship prices for every task. McKinsey’s 2025 State of AI survey confirms the pattern - adoption is increasing, but impact at scale remains elusive for most organizations.

Now CIOs have stopped asking which model, but where each workload needs to run and how much it’s going to cost.

Inference Cost Inflation

Banks delivering the next best action are a good example: the in-app, in-branch, or call-center recommendation served in milliseconds against a customer’s live context. The best banks prove that personalization at this layer can lift revenue by 5-15%. To give a firsthand example, a global bank we work with launched an AI assistant that has already resolved more than 1.5 million customer inquiries in its first year, driving huge efficiencies.

But the inference economics are unforgiving at this scale. A single agentic decision can chain five to twenty model calls, each carrying its own context window. The cost gap between £0.50 and £3 per million input tokens seems trivial in a single-turn demo. Spread across hundreds of millions of customer events, it becomes the difference between a money-making feature and a money-burning one.

This isn’t a hypothetical either. Uber’s 5,000-strong engineering team’s use of Claude Code burned through the company’s entire annual AI budget in the first four months of this year. And AI companies are responding to this market shift. Decagon, after re-architecting onto an open-source multi-model stack on NVIDIA Blackwell, dropped cost per voice query by sixfold. Next best action isn’t a marketing decision anymore; it’s an economic decision.

Organizations making the structural shift now will outcompete those treating model selection as an afterthought.

Complexity Doesn’t Disappear, It Just Moves

The hardest lesson of the past 18 months is that model commoditization does not reduce enterprise complexity but relocates it. Open weights from Mistral or DeepSeek cut experimentation cost, but orchestration, governance, evaluation, and integration burdens move up the stack and sit with the buyer.

Enterprise leaders should be measuring unit economics per useful task, operational burden per deployed agent, and the ratio of inference spent on the governance scaffolding around it. That ratio is typically 1:5 or worse.

A second architectural shift is arriving: sub-quadratic attention.

Approaches from DeepSeek, Google, and Cartesia are collapsing the cost of long-context reasoning by orders of magnitude, with recent benchmarks showing 100x to 300x cost reductions at comparable accuracy.

Large banks will now be able to run whole-portfolio risk modelling, multi-decade fraud detection and cross-jurisdiction Know-Your-Customer (KYC) as single-pass operations - no more chunked retrieval workarounds.

Telcos can make network operations, predictive maintenance and multi-year customer journey reasoning more economically viable at scale. And manufacturers can move full-plant simulation and supply-chain disruption forecasting from periodic batch jobs to continuous reasoning.

The architecture that wins will not be the one with the cheapest token. It will be the one that places compute closest to the data, under the right jurisdiction, with governance that holds.

Sustainable, sovereign, controlled - that’s the new triad. The enterprises that build for it now will define the next decade.

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Artificial intelligence (AI) is rapidly becoming embedded in the modern workplace, with employees are increasingly turning to AI tools to work more efficiently and boost productivity.

This growing demand for faster, more effective ways of working is driving the rise of shadow AI - the use of AI tools outside approved organizational controls and governance frameworks – which results in organizations quickly losing visibility into data usage and potential risks.

The scale of this challenge is significant. While 90% of executives are confident in their organizations' visibility into AI tools, just 52% of employees admit to using AI tools without approval, often through personal accounts.

As a result, organizations are left grappling with a widening gap between AI adoption and AI governance.

The next frontier of AI risk

When AI is used without formal oversight, it can bypass governance controls, increasing the risk of errors, regulatory breaches and sensitive data leakages. Organizations are most exposed when AI is already influencing business-critical activities, from customer service and operational decision-making to software development and content creation.

The challenge will intensify as businesses move beyond large language models, which generate information, to large action models and agentic systems that can take action. These systems can diagnose issues, recommend actions and execute workflows with minimum human input, increasing both the speed and scale at which mistakes occur.

A shadow agent operating outside approved governance frameworks could trigger harmful actions before organizations have the visibility and governance capabilities needed to intervene.

There is also a longer-term risk that future AI systems will be trained on synthetic or lower-quality data, weakening performance and decision-making over time. Transparency and traceability will be critical to maintaining accountability, protecting ethical standards and preserving the effectiveness of AI systems as adoption continues to accelerate.

AI governance as an enabler

What works is AI governance that enables innovation while putting clear guardrails in place that are integrated, transparent, auditable, and aligned with existing risk and compliance frameworks. If AI is to be used safely, firms must be able to successfully identify exactly what went wrong and why when issues arise.

In practice, mature governance starts with an approved AI tool stack that provides safe and trusted options for common use cases. This should be supported by risk-based policies that make clear the data being handled, what can and cannot be shared, which tools are permitted, and where human approval is required.

Low-risk tasks such as drafting or summarizing content should not be governed in the same way as high-risk uses involving customer data, regulated information or business-critical decisions.

Training is equally important. The challenge, beyond only enforcing controls, involves helping employees understand why those controls exist and how to use AI responsibly. As agents increasingly diagnose issues, recommend actions, and execute workflows with minimum input, human oversight and approval processes must scale alongside them.

Interoperability will be critical to making this workable at scale, allowing organizations to operate across jurisdictions and multiple AI models without repeatedly rebuilding governance processes and systems from scratch.

Making responsible adoption the easy choice

For security and compliance leaders, the goal should be to make responsible AI adoption the path of least resistance. Employees turn to shadow AI when approved tools are unavailable, difficult to access or fail to meet their needs. Companies that focus solely on restricting usage risk driving activity further underground and losing out on the efficiency and innovation gains that AI can deliver.

Organizations that successfully balance AI productivity and control over their systems recognize that shadow AI use is often a symptom of unmet demand. Employees typically turn to unauthorized tools because they are easier to access, faster to use or better suited to the task at hand. Rather than focusing on restrictions alone, leaders should understand where AI is already being used across the business and ensure approved alternatives are available for the most common use cases.

With three-quarters of office professionals saying they would be likely to look for a new job that offered better AI skills development, firms that combine governance with opportunities to build AI skills are likely to see stronger adoption of approved tools and, as a result, less reliance on shadow AI.

Building an AI-enabled culture means giving employees the tools, knowledge and confidence to innovate within clear boundaries. By doing so, shadow AI can be reduced while the speed and agility that workers increasingly expect is maintained.

The organizations best positioned to succeed

The businesses that strike the right balance for AI success will be those that view governance as a foundation for AI adoption and not a barrier to it. By making the secure, approved path the easiest path, shadow AI risk is reduced without sacrificing productivity.

Embedding strong governance, supported by trusted and well-managed data foundations, avoids costly mistakes and allows AI to be deployed and scaled with greater safety and confidence.

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• Private drone companies can now request access to US Army testing ranges
• Companies no longer need existing government partnerships before requesting range access
• Five military facilities across America and Morocco are joining the programme

Private drone companies without existing government contracts can now request access to the US Army test ranges nationwide.

The arrangement removes a requirement for companies to have existing government partnerships before requesting access to military ranges.

Army Secretary Dan Driscoll framed the change as an effort to cut through bureaucratic obstacles facing smaller defense contractors.

What companies can access and where

Five ranges now fall under this new access system, spanning four US states and one overseas location.

Dugway Proving Ground in Utah specializes in long-range fires testing for companies developing precision strike capabilities, whilst West Cibola Range in Arizona currently focuses exclusively on drone and counter-drone system evaluations.

Camp Shelby in Mississippi and Camp Grayling in Michigan round out the domestic testing locations available.

The fifth site, the Africa Multidomain Training and Experimentation Center, sits in Morocco.

Beginning in September 2026, Camp Grayling will host a recurring test simulating degraded electromagnetic conditions found in Ukraine.

This quarterly event will let companies trial drone and counter-drone systems against jamming and disrupted signals.

To get access to any of these sites, companies will have to apply through testrange.army.mil, though the Army cannot guarantee a specific site or timeline.

"A company with a good idea shouldn't need a team of lawyers and a program of record just to prove their equipment works," said Dan Driscoll, Army Secretary.

"So we fixed that. One front door — testrange.army.mil — a real person to walk you in, and on the other side, everything the modern battlefield demands: contested airspace, degraded signals and the space to test at real scale."

The pressure driving this shift

The expanded access reflects urgency inside the Pentagon around accelerating drone and counter-drone development timelines.

The Defense Department recently formed Joint Interagency Task Force 401, a unit built to streamline counter-drone procurement.

That task force has already stood up an online marketplace connecting military buyers directly with technology suppliers.

Behind this push sits a separate concern, with reports suggesting depleted Patriot missile interceptor stockpiles nationwide.

The United States reportedly used roughly two-thirds of its Patriot interceptor inventory during its recent conflict with Iran, as Washington appears to have underestimated how quickly a prolonged confrontation with Iran could consume its most valuable air-defense interceptors.

However, Defense Secretary Pete Hegseth disputed a CNN report claiming military commanders had flagged critically low interceptor stockpiles.

Regardless of that disagreement, the Pentagon continues pressing defense contractors toward faster production of essential weapons systems, and the range initiative sits within a broader goal of hastening development for drones, counter-drone tools, and interceptors.

Leading this effort is the US Army Test and Evaluation Command, working alongside several partner organizations nationwide.

Those partners include the Mississippi and Michigan National Guard units, along with U.S. Africa Command's regional support.

For companies previously locked out by lengthy contracting requirements, this shift represents a meaningfully lower barrier to entry.

Via Defense News

• India is replacing the Su-30MKI's ageing Russian radar with Virupaksha
• The new radar could detect conventional fighters 300 to 400 kilometres away
• Virupaksha is expected to detect stealthier aircraft beyond 200 kilometres

India's Su-30MKI fleet, built on a Russian airframe design, is now getting a distinctly homegrown upgrade to its detection system.

The Defence Research and Development Organisation is developing a new radar called Virupaksha to replace the aircraft's ageing Russian-origin unit.

This radar swap forms a core element of the broader Super Sukhoi modernisation programme currently awaiting final government clearance.

Indian radar could transform the Su-30MKI's detection capabilities

The Super Sukhoi programme aims to elevate the aircraft to a more advanced fighter jet class through this sensor overhaul.

In simple terms, that means the jet would gain sharper detection abilities without needing an entirely new airframe.

Indian officials have estimated the cost for an initial batch of 84 aircraft at ₹60,000 to ₹65,000 crore (about $6 billion), but it still awaits final approval from the Cabinet Committee on Security.

Astra Microwave Products, a private contractor, is building a key radar component, the Active Antenna Array Unit (AAAU), alongside DRDO engineers.

The AAAU works like the radar's eyes, scanning the sky for threats electronically instead of using a spinning dish.

It houses roughly 2,400 small transmitting and receiving parts made from a material called Gallium Nitride, which is more power-efficient than older materials.

In practical terms, this material lets the radar see farther, run cooler, and resist enemy jamming more effectively than older designs.

It also keeps the whole unit relatively lightweight, which matters when fitting new technology into an existing aircraft.

Once complete, the radar is expected to detect conventional fighter-sized aircraft between 300 and 400 kilometres away.

Against stealthier aircraft designed to evade detection, its range is still expected to exceed 200 kilometres, a major improvement over the current system.

Virupaksha radar moves closer to completion

Astra Microwave has told investors that its portion of the radar programme remains on schedule for completion within months.

The company expressed hope that the full Virupaksha radar system could be finished by the end of this year.

"The Active Antenna Array Unit remains on schedule despite the scale and complexity of the underlying technology," said a company spokesperson.

Completing this radar on time would mark a meaningful milestone for the wider Super Sukhoi modernisation effort.

It would also suggest that India's domestic defence electronics industry can deliver advanced radar systems without major delays.

Whether the full 84-aircraft upgrade eventually proceeds still depends on Cabinet approval and future budget decisions.

Until that approval arrives, the radar's fabrication progress remains the clearest sign of how quickly the broader programme is moving forward.

For a country still reliant on imported fighter jet technology, a homegrown radar like this represents a notable shift in capability.

It shows India attempting to pair a foreign-designed airframe with an increasingly self-built sensor and detection system.

Via IDRW

• America's aging nuclear missiles depend on data scattered across dozens of systems
• Minuteman III could remain operational decades beyond its original retirement schedule
• The US Air Force wants one AI interface to organize fragmented missile data

The US Air Force is searching for an artificial intelligence agent capable of centralizing the fractured data behind its aging nuclear missile fleet.

An active industry notice indicates that the Minuteman III program relies on roughly 60 disparate, disconnected systems.

Those systems range from local hard drives to shared networks, and no single tool currently governs them all.

Centralizing decades of missile data

Sustaining the decades-old missile fleet counts as a national security imperative under a mission commonly called “no fail”, since failure could bring catastrophic results.

The US Air Force must continuously keep 400 missiles ready to launch at a moment's notice, a responsibility that leaves little room for error.

According to the notice, the requested AI agent should construct a single unified interface that pulls information from design drawings, maintenance records, and other scattered sources.

Previous attempts to build a centralized digital ecosystem for the missile's lifecycle have failed for a range of unspecified reasons.

Officials expect the new system to collate information in near real time and reduce manual compilation by an amount still to be negotiated with a winning contractor.

The notice adds that the initial phase will cover only data marked as controlled unclassified information, though that scope could later expand to include secret-level material.

The Minuteman III missile first entered service in 1970, and its originally planned retirement year of 2036 would have made it exactly 66 years old.

That 2036 timeline has since slipped, however, as delays to the Sentinel replacement program have pushed the schedule further out.

A Government Accountability Office review found that extending Minuteman III operations to 2050 remains feasible, though it carries significant sustainment risks.

Under that revised outlook, the fleet's oldest missiles could approach 80 years of service.

Officials stress that AI will play no role in launching the missiles

The notice does not describe any AI role in launching or operating the nuclear missile fleet, and officials insist that such decisions are not for AI tools, but remain in human hands.

This search for a data tool follows years of delay facing the Sentinel program, which is meant to eventually replace the Minuteman III fleet entirely.

Because Sentinel remains years away from deployment, the aging missiles must keep functioning far longer than military planners originally anticipated.

The effort also arrives as the Defense Department pushes AI adoption across its ranks and seeks hundreds of additional software engineers.

Other Air Force programs have tested similar autonomous capabilities recently, including a modified F-16 that used AI to analyze sensor data during a live intercept exercise.

Before the latest notice, the Pentagon had separately awarded a $31 million contract to Air, a firm formerly known as Govini, to support ICBM fleet sustainment.

That company said its work would help build a clearer view of the missile program's industrial base and supply chain risks.

It remains unclear how that platform might eventually connect with whatever AI agent the Air Force ultimately selects for its centralized data project.

Given the scope of the request, the Air Force appears more focused on modernizing data management than on introducing autonomy into weapons decisions.

Whether contractors can deliver a workable system for such a sprawling, decades-old infrastructure remains an open question.

Via Breaking Defense

The Tesla Optimus, also known as the Tesla Bot, is one of an entire catalogue of humanoid robots that manufacturers have unveiled in the last few years. Humanoid robots may look the part, and while they may be a few years away from maturation, executives like Tesla CEO Elon Musk are adamant they're a direct substitute for human labor.

Optimus Prime

Before Musk had unveiled the first prototype of the Optimus robot, he was speaking to its potential and the role it could play in the workforce of the future.

Speaking at the Wall Street Journal CEO Council Summit, Musk reportedly said that the robot could one day be good enough to replace human labor, just as machinery in the last few decades has gradually replaced humans in production lines.

His comments also suggested that rather than eliminating jobs, and thus leading to mass unemployment, the rise of humanoid robots will fill a gap in labor demand that currently exists, allowing corporations to make more and build quicker. He was adamant, in these comments and in several remarks since, that there aren't enough people and we aren't reproducing enough to maintain our current population levels.

Robotic replacement

Musk is adamant that the rise of humanoid robots will not impact the level of available jobs – but that doesn't mean his views will play out.

Many disagree, suggesting there is a material risk to current roles, including those that involve repetitive or manual tasks. But experts disagree over whether this means a net reduction in jobs, or simply a shift in the demand for human roles in the future.

Many suggest 2027 will be the year in which robots enter a serious level of production. While the Optimus robot is not currently being made, Tesla is targeting next year and beyond for mass manufacturing and distribution following several delays to date.