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When organizations first started formalizing AI policies, the problem they were solving for was narrow: keep employees from sharing sensitive data with a public model. A clear, manageable risk with a clear, manageable response.

What those policies didn't account for was how quickly AI would evolve, or how far the organizational guardrails would fall behind it.

Most companies are still running governance frameworks written for that original moment, applied to a technology that looks nothing like it did then.

What happens when outdated governance meets AI agents

The governance frameworks most organizations are running were designed to manage exposure, not enable work—and that design shows.

Organizations are running agents that autonomously query databases, update records, and trigger downstream workflows, across connected systems, at a pace no approval cycle was designed to match.

Those agents can be governed, but vague policies won't cut it. The governance has to be specific enough to translate into actual system-level constraints, like which systems the agent can access and under what conditions.

Governance that can't keep up with the technology it covers isn't governing anything. It's just documentation .

4 questions to audit your AI governance framework

1. Can your employees find out right now what AI can access on their behalf?

When someone deploys an AI tool or agent at work, that tool is often connected to real systems ( email , CRM , databases, and calendars) on behalf of the person using it. Without a governance framework that clearly outlines what those connections look like or what the tool is authorized to do within them, your organization doesn't have a reliable way to assess what's actually exposed.

To make this clear, build and maintain a permissions inventory: a living record of which AI tools are approved, which systems each tool can connect to, what actions it's authorized to take, and which team or individual owns each integration. If your organization uses an AI governance platform, much of this can be tracked and managed there rather than maintained manually.

Either way, it doesn't need to be overly sophisticated out the gate. But it does need to be current discoverable.

2. If an AI agent takes a wrong action, how quickly can you revoke it?

Agents take actions, sometimes sequences of them, across connected systems. When something goes wrong, the ability to stop it quickly depends entirely on how access was set up in the first place. If credentials are scattered across sessions, scripts, and individual tool configurations, revoking access to one system means tracking down every place that credential was used.

Consolidating agent credentials under a centralized auth system changes that. Each agent operates under a defined identity with explicit, scoped permissions, so removing access is a single action with a clear audit trail and no cleanup exercise required.

Standards like the Model Context Protocol (MCP) are designed specifically for this. MCP can give agents a structured, auditable channel to access external systems through OAuth rather than credentials embedded in prompts or scripts. For organizations evaluating how to centralize agent access, it's worth understanding what's possible when the connection layer itself is built with governance in mind.

3. Does your governance policy describe what's permitted, or only what's prohibited?

A policy built around prohibitions tells employees what they can't do, but it doesn't tell them what they can. For humans, that leaves a gray zone they have to interpret. For agents, the problem is more concrete: an agent given a prohibition list and no permitted-use definition has no reliable boundary for what falls inside or outside its scope.

The fix is to define use cases affirmatively: approved tools, permitted system connections, and authorized actions. That gives both employees and the agents they deploy a clear framework to operate within, and it makes the governed path the default one.

4. Does your governance framework specify what your AI agents can access and act on, at the system level?

A framework that addresses AI in general terms (for example, responsible use and acceptable outputs) gives humans enough to make informed decisions about how they use AI.

Agents require something more specific. When you deploy an agent, it operates based on two things: the instructions and context it's been given, and the systems it's been granted access to. If your governance framework isn't precise enough to be included in that context, then it isn't governing the agent. Governance that actually covers agents defines access at the system level: which systems, which actions, and under what conditions.

In practice, that looks like provisioning agent access through an identity and access management system, assigning each agent a defined role with scoped permissions, and logging every action it takes against those permissions.

Build for what's next, not just what's now

AI capabilities are moving fast enough that some of what's true today about how agents operate will look different in a year. Governance frameworks that are current now will need to be revisited.

Getting the fundamentals right today matters precisely because the landscape keeps shifting. That means treating governance as an ongoing operational practice: reviewing access definitions when new tools are deployed, auditing permissions when agent capabilities expand, and updating permitted-use frameworks when the technology changes scope.

The cadence matters as much as the content.

The principle that holds, regardless of what AI looks like next year, is this: governance that can't keep pace with the technology it's supposed to cover isn't governing anything. Building governance that's designed to evolve is the work.

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Clearing your raider for Expedition is a pretty big deal in Arc Raiders . It involves tons of prep and a dedication that you're willing to start the next season with a fresh character and stash (alongside some perks that the Expedition offers your new avatar). It's not something that everyone is willing or able to do, which is why this latest bug left players in such disarray.

The bug in question impacts those who manage to finish the 100K damage challenge: Once completed, players would be automatically signed up for the upcoming fourth Expedition, whether they wanted to be shoved out into the wasteland or not.

[Embark] Update on Expedition sign-up bug - Sign-ups reverts starting tomorrow Friday, July 17, 2026 5:00 PM. (done in waves) from r/ArcRaiders

Thankfully Embark is intervening now, before anyone's exiled Topside. "We want to err on the side of caution," Embark explains in a message to players via social media . "We will revert the Departure sign-up for players whose sign-up behaviour appears representative of this issue. This may be done in several waves, starting tomorrow, Friday, July 17, 2026 5:00 PM."

But even with the intervention scheduled for the weekend the devs still advise players to "check the in-game Expedition tab and verify that it shows the correct information for your case," just in case your raider is still ready to be sent off.

"If you had previously been signed up for departure automatically and this does not revert by the end of day tomorrow, please reach out to our support team as soon as possible. If you have changed your mind and want to depart, you can still opt in to do so. Additionally, Last Call will be available as usual for those of you who aren’t able to sign up for Departure before the deadline."

It seems like Embark are on top of the situation, but there are still some players who are deciding to take extra precautions: "I'm taking a screenshot of my intentional signup, damage completion and Expedition vault contents so I can open a ticket when they fuck this up," one declared on Reddit . I guess it is better to be safe than sorry.

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Linux likers and AI dislikers, it's time to raid the cutlery drawer and fork your favourite distro. Why? Because Linus Torvalds has declared that Linux is not an "anti-AI" project.

Torvalds recently responded to a discussion about Sashiko, an agentic Linux kernel code review system, writing, "I realize that some people really dislike AI, but this is an area where I'm willing to absolutely put my foot down as the top-level maintainer. Linux is not one of those anti-AI projects." His full response has been preserved via the archive for Linux kernel mailing lists, lore.kernel.org (via The Register ).

If users and developers don't like the project using AI, Torvalds instructs them to either fork their distro "or just walk away". Torvalds goes on to argue, "AI is a tool, just like other tools we use. And it's clearly a useful one. It may not have been that "clearly" even just a year ago, but it's no longer in question today."

He also notes that "yes, [AI] can also be a somewhat painful tool, both for maintainer workloads and just from a 'it keeps finding embarrassing bugs' standpoint." But, not dissimilarly to Microsoft and Firefox , he is apparently now welcoming AI as a tool that can help make the maintainer bug-hunt a little less painful.

It's not a complete 180 from Torvalds—even if, back in May, Torvalds has previously lamented how the use of LLMs to find bugs was wasting a lot of the Linux team's time. He explained then, "The continued flood of AI reports has basically made the security list almost entirely unmanageable, with enormous duplication due to different people finding the same things with the same tools."

Before then, during the Open Source Summit in 2024, Torvalds had described AI as " 90 per cent marketing and ten per cent reality ." He also said: " Let's wait 10 years and see where it actually goes before we make all these crazy announcements." It would appear, as far as Torvalds is concerned, we didn't even need to wait that long.

In his latest missive, Torvalds goes on to write: "We're not forcing anybody to use it, but I will very loudly ignore people who try to argue against other people from using it. And no, AI isn't perfect. But Christ, anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time. Because it's not like natural intelligence is always all that great either."

Torvalds also writes that, "the kernel project has been and will continue to be about the technology," and he concludes his missive by saying, "we make decisions primarily based on technical merit. Not fear of new tools."

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Universal fit with cooling, improved coverage and better battery life

On sale from 29 July 2026, pre-orders open now

It's £339.99 (about $459 / AU$655)

We really rate the Shark CryoGlow LED face mask, giving it four and a half stars out of five in our Shark CryoGlow review . Our only real niggle was the relatively short battery life, so we're pleased to see a newer version with more stamina and even better facial coverage. Say hello to the Shark CryoGlow Plus, which promises to deliver everything we liked about the original with some important improvements.

As before, the CryoGlow Plus uses three kinds of LED light: blue, red and deep infra-red. Coverage has been improved 120% around the crows' feet area in this new model and it's more energy efficient too.

(Image credit: Shark Beauty) Shark CryoGlow Pro: key features and price

The CryoGlow Plus has Shark's InstaChill Cold technology, which provides two active cooling points to help soothe the under-eye area. The mask's Under-Eye Revive Mode has three cooling levels and you can use it solo or alongside any of the LED modes.

There are three LED modes: Better Ageing Mode, which is a six-minute treatment using red and deep infra-red light; Blemish Repair Mode, which is an eight-minute, three-phase routine using blue and deep infra-red before finishing with red LED light; and Skin Sustain Mode, which is a four-minute maintenance treatment using red, blue and deep infra-red.

In our time with the original CryoGlow we were impressed by its performance, with the important caveat that your mileage may vary because everybody's skin is different. We particularly liked the InstaChill feature, which really did de-puff the under-eye area. Provided you keep up your use of the mask, we found it to be the best alternative to regular, professional skincare treatment.

The CryoGlow Plus is only marginally more expensive than its predecessor: that had a list price of $349 / £299 / AU$699, while the CryoGlow Plus is £339 in the UK. US and Australian prices haven't been announced yet but that's about $459 / AU$655 before taxes and tariffs.

The CryoGlow Plus officially launches on 29 July but you can pre-order from SharkNinja.co.uk and John Lewis now. It'll be available in two shades, Pastel Berry and Pastel Dusk.