Feed aggregator

• Android Pulse has appeared on the Google Play Store
• It's available as an update for some users, showing that it's already on their phones
• The description suggests this runs in the background to check for misbehaving apps

Android Pulse is an app that you probably don’t know you have — but if you’ve got an Android phone then there’s a chance it’s already running in the background, and there’s an easy way to find out.

As spotted by 9to5Google, just such an app has appeared on the Google Play Store, and more tellingly, you might find it in your list of available Google Play Store app updates — meaning it’s already on your phone, even though you won’t have knowingly downloaded it.

This isn’t actually a completely new app, with versions of it dating back to March appearing on APK Mirror, but it has seemingly only now got itself a Google Play listing — and with the listing mentioning over ten million downloads, a lot of people must already have it.

Keeping your Android's pulse healthy

(Image credit: Google / 9to5Google)

So what is this mystery app? Well, the listing says “this app facilitates rule distribution for anomaly detection in critical system resource consumption by the Android operating system and installed applications.” That’s a bit unclear, but 9to5Google’s interpretation — and we’re inclined to agree — is that this runs in the background, performing software diagnostics on the operating system and your installed apps.

It’s likely — based on the “anomaly detection” part of the description — that it’s looking for any apps or services that are misbehaving, by for example using more battery or CPU than they should. Though whether it then does anything about them or just alerts Google to it is less clear.

Either way though, the ultimate goal is presumably to keep Android running smoothly, so that’s no bad thing.

But this isn’t an app you can directly interact with or run scans from — if it’s on your phone, you just have to hope and assume it’s doing some good, and not simply wasting resources.

• The first teaser trailer for The Pitt season 3 has been released by HBO Max
• It picks up on Dr. Robby's first day back after his four-month sabbatical
• A release window of January 2027 has been confirmed for the season

The Pitt season 3 first trailer has dropped, and I'm incredibly surprised that we're getting a first look this early. We can expect new episodes to arrive at some point in January 2027 — meaning we've got a five month wait until we find out what's really going on.

But HBO Max is a streaming service that likes to keep us on our toes, simultaneously giving away everything and nothing in the first season 3 teaser trailer (which you can catch up with below).

We already know that season 3 is set in early November, corresponding with the three- or four-month sabbatical Dr. Robby (Noah Wyle) is due to take at the end of season 2.

While creators said in a statement that season 3 is “just before the holidays, ushering in a whole new set of emergencies and confrontations and complications,” Wyle added at PaleyFest, "The thesis of season 1 is the doctor is the patient. Season 2, doctors don’t make very good patients. Season 3, doctors benefit from being patients.”

He also told Deadline, "Well, I think we’ll find out what that rock bottom [for Dr. Robby] looks like next year.”

Is this the impression I get from watching the first few glimpses of footage? No. But instead, I think that another medic is finally about to have their moment in the spotlight.

Opinion: The Pitt season 3 teaser trailer hints that Dr. Javadi will finally step up

With such a truly ensemble cast, there were never going to be any losers in The Pitt season 3... but if I were you I'd keep your eye on Dr. Javadi (Shabana Azeez) in new episodes.

Meek, mild-mannered and constantly second-guessing herself, as the 'baby' of the ER team Javadi has striven to prove herself both to her colleagues and to her disapproving parents, who work in general surgery and endocrinology upstairs.

But in the above clip, she's finally stepping up to take charge... and it suits her. In a split-second clip, we see her working with two other medics to try and resuscitate a patient, asking for help without feeling self-conscious in the process.

It's rare that The Pitt produces positive stories of growth for our team, but as the ER storm whips back up, I've got a good feeling about Javadi.

From what Dr. Robby tells us, though, nothing else is going to be straightforward. How the screaming casualties rolling into the day shift are going to differentiate from seasons 1 and 2 is yet to be established, but put it this way — nobody's looking well-rested.

Today, Treasury Secretary Scott Bessent is set to announce details on new economic pressures on Iran. And, Canada has vowed to impose retaliatory tariffs against the U.S.

(Image credit: Alex Wong)

• Amazon has increased the prices for its best-selling devices, including popular Kindle models
• This is due to the increasing memory and storage component costs
• Users online have found ways to score Kindles for cheaper

Amazon is the latest tech giant to increase prices across its lineup of smart home devices — and this also includes popular Kindle models.

In addition to devices such as the newer Echo Dot Max, Echo Show 21, and some Fire TV sticks, Amazon’s basic Kindle and mid-range Kindle Paperwhite e-readers have shot up by up to 60%. The only Kindle model that hasn’t seen a price hike is the premium Kindle Scribe.

Amazon’s price increases quietly landed over the weekend without an official statement from the retailer, but luckily the online outlet Fortune spotted the new price tags when conducting a review — who also shared that Ring doorbells are the only Amazon devices that haven’t been hit with a price adjustment.

The outlet also provided a full breakdown of the devices affected by the price hike, which you can view below:

Device

Old price

New price

Echo Dot (5th Gen)

$49.99

$79.99

Echo Spot

$79.99

$109.99

Echo Show 21

$399.99

$499.99

Echo Dot Max

$99.99

$119.99

Echo Show 15

$299.99

$349.99

Echo Show 11

$219.99

$249.99

Echo Show 8

$179.99

$199.99

Fire TV Stick 4K Max

$59.99

$84.99

Fire TV Stick HD

$34.99

$39.99

Kindle (16GB)

$109.99

$149.99

Kindle Paperwhite (16GB)

$159.99

$199.99

eero 7 (3-pack)

$349.99

$399.99

eero Pro 7 (3-pack)

$699.99

$799.99

Though Amazon didn’t issue any form of official explanation for this, a spokesperson released a statement to Fortune to offer more insight, sharing that consumer tech is “facing significant increases in memory and storage component costs. After absorbing these increases for as long as we could, we recently adjusted pricing across our product lines”.

2026 really has been the year of the price hike for the tech world, and it’s not just Amazon that’s been affected. Apple raised its Mac, iPad, and HomePod prices in June, while services like Spotify hiked subscription plans earlier this year and the same goes for YouTube Premium.

Price hikes are regular as clockwork at this rate, however some users don’t seem to mind as much if the product in question is of value and has a lot to offer. But take devices such as the standard Echo Dot and basic Kindle for example, two entry-level models in their respective areas, a $30-$40 increase seems a little unfair.

Though the Echo Dot is a basic speaker that gets the job done, its audio output and smart features aren’t worth over $50 in my opinion. The same goes for the basic Kindle, an e-reader with basic functions, which you also have to pay more for if you want an ad-free experience.

As you can imagine, Amazon’s latest price increase hasn’t settled well with Kindle users, especially since Amazon’s e-readers are one of the company’s best sellers. Redditors have been posting non-stop about this, one user replied to a post saying ‘I hope nobody buys these and prices go down’ — however, bookworms are sharing ways to score these devices for cheaper in the wake of the price hike.

Comment from r/ereader

Now, users are reverting to third-party discount sites to get their hands on Kindle e-readers for a fraction of the new prices. Many users replied under the same Reddit post recommending Unclaimed Baggage, a site that sells on devices found in lost luggage from airlines.

For example, Amazon’s premium Kindle Scribe Colorsoft e-reader is the best part of $680, while Unclaimed Baggage has offers for half of that. This isn’t the only third-party retailer however, Redditors have also recommended Woot, an online deals site run by Amazon where you can access hundreds of deals across electronics.

One of the better sides to these third-party options is that they’re not limited to Kindles, they also have a number of listings for other devices in Amazon’s latest price increase. That said, Redditors aren’t ruling out Facebook Marketplace for finding offers on gadgets — you never know what the person around the corner from you wants to get rid of.

• HoverAir launched its new Versa drone on Indiegogo, saying it had FCC approval
• FCC has since stated that certification was granted 'erroneously'
• The Versa is the world's first 2-in-1 pocket gimbal camera and drone

HoverAir launched the Versa drone on Indiegogo last week, and a few words in the promotional material suggested that the world's first 2-in-1 pocket gimbal camera and drone had a distinct advantage over DJI: "FCC (Federal Communications Commission) certification."

The claim of FFC approval implied that the Versa would be permitted to be sold in the US, a market which new DJI drones have been fully locked out of since 2025. World-first features, plus US availability — those were two big leg-ups for the Versa.

However, HoverAir's hopes of having that key advantage in terms of market access have quickly been dashed. The FCC contacted TechRadar to tell us that "certification for this device was granted erroneously and has been set aside by the FCC and removed from our public database".

Furthermore, it said the "FCC is considering further action against HoverAir for apparently willfully violating the FCC’s Covered List rules."

It's quite the turnaround for HoverAir. The company must have been licking its lips at the prospect of its innovative innovative 4K drone and pocket gimbal camera having unchallenged access to the US market. Turns out, it's in the same position as DJI after all.

Here's the FCC statement in full:

“Importing, marketing, or selling this [HoverAir Versa] device in the United States is a violation of U.S. law, and the FCC has directed HoverAir to cease any such activity immediately.

"The device in question is on the FCC’s Covered List: the Versa camera alone is a prohibited UAS critical component produced in a foreign country and, when the Flight Kit is included, the device falls under the ban on UAS produced in a foreign country.

"The certification for this device was granted erroneously and has been set aside by the FCC and removed from our public database. The FCC is considering further action against HoverAir for apparently willfully violating the FCC’s Covered List rules.

"Finally, the FCC is sending guidance to all Telecommunications Certification Bodies to strengthen their due diligence of applications to ensure they comply with their legal obligation not to certify equipment on the FCC’s Covered List."

HoverAirHoverAirHoverAirHoverAirMore bad news for drone fans in the US

Following the self-flying X1 and the waterproof Aqua, HoverAir notched up a trio of world-first drones with the Versa, a 2-in-1 pocket gimbal camera and drone.

According to its Indiegogo page, the Versa is tipped for a worldwide launch and for October 2026 delivery to backers. However, despite US pricing (starting at $499) remaining on the crowdfunding page, it looks like the Versa won't be taking to the skies in the US any time soon.

That's a shame for drone fans in the US. Versa's modular design means that its wings (guard-protected propellors) can be removed in a simple motion, switching the device from a 230g self-flying 4K camera to a 163g pocket gimbal camera.

Its key features include 4K video up to 60fps, 4K HDR video up to 30fps with 10-bit color depth, 12MP photos (there's no mention of RAW format) and a 1/1.3-inch sensor.

Controller-free aerial selfies are a breeze, thanks to palm take-off and landing, AI subject tracking, auto framing, plus voice and gesture commands.

Furthermore, the Versa is compatible with HoverAir's Beacon device, which debuted with the Aqua, meaning it can pair with a phone for clear audio. It's said to reach speeds up to 36km/h with a Level 5 wind resistance, and have 14-minute flight times, though realistically battery life will be less than that.

As a gimbal camera, the Versa features a rotating OLED haptic screen, which measures 1.64 inches on the diagonal and hits 800 nits of brightness.

We haven't tested the Versa yet, but we'll share our in-depth review soon. The innovative drone will make its public debut at the IFA consumer electronics show, which takes place in Berlin, Germany, from September 4-8, and you can learn more about it in the YouTube video below.

The first salvo of the war against AI slop on LinkedIn looks to be going the right way for those of us who can actually use our imaginations, the company has said.

LinkedIn Chief Product Officer Hari Srinivasan has revealed over a million people have clicked the ‘seems like AI slop’ report option in the two weeks since its launch - and I've definitely clicked it a few times.

Srinivasan says that these reports, along with changes to LinkedIn's detection systems, have cut views of content classified as AI slop by 40% - although sadly my feed is still pretty full of it.

"Low-quality content" crackdown

In his post, Srinivasan noted the feedback was, "helping us better understand how our community experiences low-quality content" across LinkedIn, suggesting stricter clampdowns to come.

The first stage of this is launching now, so if one of your posts receives enough community feedback (aka is reported as AI slop), you may see a message in your Post Analytics, "to help you understand how your content is being received".

"We approached this assuming good intent; I know I'm increasingly conscious on how to not sound like AI & the goal is to provide helpful feedback," Srinivasan says.

"Importantly, no single piece of feedback determines how content is distributed," he added. "We look at many signals together, and we've built safeguards to help prevent individual feedback from being used to unfairly target other members."

"Despite the progress, we know we have more to do to ensure LinkedIn remains a place where you can find real people & real perspectives... this all remains very top of mind," Srinivasan concluded, suggesting further actions may come soon.

(Image credit: LinkedIn)

The company announced it was cracking down on AI slop in early August 2026, when Srinivasan declared it was, "a top priority for all of us. We really care about this...people come to LinkedIn to connect with real people and share their real perspectives, ideas and expertise."

To report a post, users just need to click on the three dots at the top-right of any LinkedIn post, and among the usual options to save, share or embed, you'll see a new option - "Seems like AI slop".

Srinivasan added that Microsoft-owned LinkedIn will, "continue to improve and invest in our automation defences..on comments alone, everyday we are now catching hundreds of thousands of automated comment attempts, and have blocked billions of other automation attempts (posting at scale, slop) in the last couple months alone."

At 2 a.m., an automated remediation agent detects a problem on the network, traces it to a misconfigured policy, validates through the harness that the proposed fix operates within the given policy boundaries and fixes it. The network stabilizes. Nobody’s notified.

In the morning, a human reviews the agent's daily insights: a summary of all the changes executed, with links to the logs, audit trails, reasoning and root cause behind them, confirms everything has been properly resolved and moves on. That is what Human-on-the-Loop looks like.

At another organization, at 4 a.m., a DIY-built, vibe-coded remediation agent detects a problem on the network, traces it to a misconfigured policy, and fixes it. The network stabilizes. Nobody’s notified. In the morning, a human reviews the logs, assumes the issue has been resolved, and moves on.

Where's the difference?

The difference is that, in the DIY scenario, the logs only tell part of the story. They don't show that the agent made three other changes to get there, which were broader than intended, and the decisions behind those changes weren’t flagged because nothing in its constraints required them to be.

This is what the move toward Human-on-the-Loop can look like without the right controls in place. No dramatic handover. Just a series of small, reasonable delegations that gradually build into something nobody explicitly signed off on.

And it's happening faster than most leaders realize. According to recent research, 57% of IT leaders expect to remove humans from the loop within a year or less, and 79% already treat AI agents as "users" who require their own identity management and governance controls.

The shift to agentic AI is happening faster than most organizations are prepared for, both in terms of governance and the ability to evaluate autonomous systems.

The illusion of "I approve"

The answer to autonomous AI has long been quite simple: keep a human in the loop. Somebody who reviews the output, hits approve, preserving accountability. Except it isn't, not really. Reviewing every action doesn't automatically create accountability, and it also prevents organizations from realizing the full benefits of autonomy.

Rather than reviewing every individual action, humans should be focused on evaluating outcomes, ensuring the system operated within its intended boundaries, and providing feedback that improves its performance over time.

Approval can become a ritual without meaning. As systems prove reliable and the number of alerts multiply, humans sometimes start to treat intervention as something that isn’t often needed.

The approval can become more of a click than a considered choice. And when something goes wrong (for example, a misconfigured policy, an automated remediation that turns into an outage), the question of who was responsible is difficult to answer.

It also reinforces a broader shift: one of the most important human capabilities becomes critical thinking and the validation of hypotheses, rather than the execution of tasks.

And something else is happening. Humans are transitioning from doing to reading before approving – a fundamental change in the day-to-day work of most of us.

Also, attribution isn't the same as provenance. A log that records what an agent did tells you almost nothing about why, or what shaped that decision. When things go wrong, those are the things you need to know.

Non-human identities and the new network population

When AI agents act on your network, querying systems or making configuration changes or routing traffic, they are effectively users. They need credentials, policies, guardrails, explainability, and audit trails just like human operators.

Most organizations haven't caught up with this. Identity frameworks were built for people, and applying AI agents to them as a kind of afterthought creates exactly the sort of shadow access that security teams spend their lives trying to eliminate.

The near-80% of leaders who already treat agents as governed identities are ahead of the curve. The rest are collecting risk they can't quantify, until it crystallizes into an incident.

To get this right you have to treat each agent as a principal with bounded permissions, time-limited access and a clear revocation path, along with a complete record; not just of what it did, but of what it was allowed to do and why.

Autonomy isn't given, it's earned

Network and security teams already know how to do this. Every enterprise has access control frameworks that govern what humans can reach and when. A junior engineer doesn't walk in on their first day with total production access. They gradually earn it, and this same logic needs to apply to AI agents.

We're not quite there yet. Too often, autonomy is treated as all-or-nothing. That isn’t the right model. Trust when it comes to humans doesn’t work like that, and it shouldn’t with AI either. Start agents in suggestion mode and let them prove themselves within clearly defined limits before expanding what they can do. And make sure every decision leaves a trail that explains not just what happened but the reasoning behind it.

As the use of autonomous AI grows, organizations will need to balance human oversight with systemic governance. People remain responsible for reviewing not only the outcomes AI produces, but, where necessary, the actions it takes and the reasoning behind them.

However, as the volume and complexity of autonomous decisions increase, this oversight should be complemented by infrastructure that embeds identity, policy enforcement, observability and accountability. Together, these controls help ensure AI actions remain transparent, traceable and aligned with organizational intent.

Building multi-agent systems that hold up under pressure

The more capable these systems become, the more organizations will move toward multi-agent architectures. This is where specialized agents each own a piece of a workflow and hand off context as they go. That's where things get complicated.

A single agent misbehaving is traceable. A chain of agents, each acting on the outputs of the last, is much harder to untangle when something goes wrong unless the right architecture and governance are in place. You need to know what each agent knew, not just what it did.

Do the boring part

Two organisations deploy the same AI-powered networking agent. One of them has done the unglamorous work: setting up tight permissions, proper identity controls and audit trails that actually answer questions. The other one hasn't.

You won't know the difference until something breaks.

We've featured the best network monitoring 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

There’s a pattern starting to emerge with AI.

At first glance, everything looks like progress. AI is being adopted quickly, embedded into products, talked about in boardrooms, and pushed into real-world use faster than anything we’ve seen before. But as businesses become more accustomed to AI and increasingly find new ways to use it, there is a greater problem brewing that has the potential to be detrimental to a company’s cybersecurity posture.

Organizations are moving quickly to use AI, but far fewer are making the right decisions about how it’s actually being delivered and secured. And the gap between those two things is widening, with security teams left scrambling to fix vulnerabilities like whack-a-mole.

The speed is understandable. AI hasn’t followed the usual enterprise lifecycle. It hasn’t patiently moved from concept to pilot to controlled rollout. In many cases, it’s gone straight from experimentation into something business-critical, stitched together from APIs, models, agents, and data sources that weren’t originally designed to work together in this way.

That creates something fundamentally different. Not just another application, but something more fluid, a tool that behaves dynamically to make decisions and interact across multiple layers of the stack in real time.

And this is where the problem begins.

Where AI security currently breaks down

While the architecture that needs to be secure has changed, the thinking around security largely hasn’t, meaning traditional security measures are still being applied to situations they aren’t built for. Most organizations believe they have this covered. They’ve extended their existing controls, added new tools and invested in visibility. On paper, it looks like a sensible evolution of what they already had that keeps up with AI.

But in reality, much of that security still sits around AI rather than within it.

These traditional methods are protecting edges, monitoring outcomes and analyzing behavior after the fact. What they’re not consistently doing is sitting in the path of execution, where decisions are actually being made, and where things can go wrong in real time. It’s this distinction that matters more than most people realize.

AI doesn’t behave like anything we’ve secured before. A single interaction isn’t just a request and a response. It’s a chain of events where a prompt is interpreted, a model responds, an agent may take action, data is retrieved, decisions are made, and outputs are generated. This all happens in one continuous flow.

The risk doesn’t exist at a single point. It exists throughout that chain. This is where prompt injection happens. It’s where models can be manipulated, where sensitive data can leak through inference and where unintended behaviors and outcomes emerge.

The cause of this isn’t always an incorrect configuration; it can also be the result of the system responding exactly as designed, just not in the way anyone expected.

The industry is starting to acknowledge this. There’s a growing recognition that runtime is where the real battle is being fought, and that securing AI means understanding how it behaves under pressure, not just how it’s built.

Moving beyond bolt-on security

But if that’s becoming clearer, why are so many organizations still getting it wrong? Well, in most cases, it comes down to how decisions are being made. AI is often being driven by innovation teams or developers, those who are closest to the opportunity and implementation of AI tools.

But that also means infrastructure and security decisions are following behind rather than shaping the architecture from the start. At the same time, there’s a tendency to default to adding more tools to plug the security gaps. Faced with a new risk, the natural instinct is to look for something new and shiny to buy that addresses it.

AI doesn’t fit neatly into that model. It doesn’t live in one place. It cuts across applications, APIs, data, and user interaction all at once. Treating AI as something you can secure with a standalone tool misses the point entirely.

What is actually needed is a different way of thinking, one that starts with looking at where control actually needs to exist. There are only so many places security can be meaningfully enforced, and for AI, one of the places that consistently matters is the flow of traffic itself.

This is the point at which requests are made, decisions are processed, and responses are returned - where behavior can be influenced the most and where policy can be enforced. Everything else, to some degree, is reactive.

This is also where the conversation around security platforms becomes more interesting. Not because AI capabilities have simply been added to existing portfolios, but because the role these platforms play is changing.

Sitting in front of applications and APIs, they have long been responsible for managing traffic, applying policy and enforcing decisions. What’s changed is that these same control layers are now being extended into AI interactions themselves.

That shift is subtle, but important, as it moves AI security away from being something that happens in isolation and closer to something that is embedded directly into how systems operate. Not bolted on, not observed from the outside, but enforced as part of the execution path.

This isn’t really about one vendor. It’s about recognizing that AI has changed the shape of the problem.

Control will define the next era of AI security

The market is still catching up. The tooling is still evolving. And most organizations are understandably feeling their way through it.

But the decisions being made now - where to place control, how to integrate security, what assumptions to carry forward from the past - will define how manageable this becomes over the next few years.

We’ve seen this before, just in a slightly different form. APIs went through a similar phase not long ago - rapid growth, fragmented control, and then a long period of retrofitting security once the risks became clear.

AI is moving faster than that ever did. The attack surface is broader, the behavior less predictable, and the consequences potentially more significant.

Which means there’s less room for getting it wrong.

The organizations that navigate cybersecurity well in the age of AI won’t necessarily be the ones that adopt AI the fastest. They’ll be the ones that understand where control needs to sit and make deliberate decisions about how it’s enforced. With AI, more than anything else, it’s not just about what you can see. It’s about where you can act.

We've featured the best endpoint protection software.

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

Cyber risk is now a board room issue, and we have seen clear examples of this in the UK. The 2025 Jaguar Land Rover attack left the carmaker with a £485m loss, swallowing up the £398m profit it had generated just 12 months before.

Production lines were halted for more than a month as the company shut down parts of its network, showing how quickly a cyber incident can affect business performance, operational continuity and the wider supply chain.

Now, businesses are facing a fresh type of threat made possible by AI – the autonomous attack. Attackers can already automate parts of target research, initial access and malware development, with any manual effort shrinking rapidly.

Simultaneously, the trust layer people rely on is eroding with the spread of AI-generated content and deepfakes. It’s a race to tackle the autonomous attack, but how do organizations formulate an effective response?

AI in a cyber-attacker’s armory

AI-driven automated technologies are strengthening a cyber-attacker’s armory. Prior to leveraging AI tools, bad actors often had to commit time and resources to researching a target company before planning an attack.

Timing was critical, and a perpetrator had to manually coordinate and initiate an attack at a specific time and could simply forget. AI doesn’t - and the rise of attack-as-a-service tools is making it possible to successfully breach organizations quickly and accurately.

Guardrails are starting to be put up around established generative AI tools, such as ChatGPT and Claude, in an effort to prevent this kind of misuse. But hackers are finding workarounds.

Rather than relying on readily available large language models (LLMs), they are deploying their own small language models (SLMs) on local devices, often on something as basic as a Raspberry Pi computer. From there, they can escalate attacks while hiding in the shadows.

The threat to businesses of all sizes

The rise of automated attacks also means that businesses of all sizes are likely to be identified by automated technology as having exploitable vulnerabilities. Small and medium-sized businesses would previously have been off the radar as attacks relied on a bad actor’s knowledge of their existence.

However, AI can now scan and process vast numbers of organizations at speed, potentially leaving smaller firms, which are less likely to have robust cyber controls in place, more exposed. And even more so among smaller businesses, defenses are typically more fragmented and less organized than AI-driven attacks.

In other words, with AI by their side, attackers can coordinate and scale far better and much more quickly than most businesses can defend.

Autonomous attacks also make third-party and supply chain risk much harder to manage. Business networks can create access to data, systems or operational processes. When attackers can automate reconnaissance and scale attacks across thousands of organizations, weaker suppliers may become an attractive route into larger businesses.

This is a particular concern because third-party risk management has often relied on annual questionnaires, point-in-time assessments and contractual assurances, but these approaches are no longer enough on their own. A supplier may have recently exposed a service, suffered a breach, changed its access privileges or failed to patch a critical vulnerability.

Businesses therefore need to move towards continuous, automated monitoring of supplier security posture.

Regulations such as NIS2 have also increased the focus on supply chain security for organizations operating in, or selling into, the EU. There is also a growing expectation from ICO and the FCA that boards can demonstrate cyber resilience.

Automation and the rise of specific attack types

Jadepuffer illustrates how AI is beginning to transform established attack types. Disclosed by Sysdig in July 2026, it was assessed as the first documented end-to-end LLM-driven extortion operation, with an AI agent conducting reconnaissance, harvesting credentials, moving between systems, destroying data and adapting when individual actions failed.

While none of the techniques were especially new in isolation, the significance was the way the AI connected them into a complete, adaptive attack.

Social engineering techniques, such as bad actors posing as trusted individuals, are becoming much more convincing in their approach. Fluent, grammatically correct messages and the professional tone and style of CEO communications can now be fully replicated on emails, SMS and even WhatsApp.

AI can even manage the entire conversation thread, including dynamically adapting responses to a target’s replies, with it possible to run simultaneous, tailored campaigns.

Vendor email compromise, where criminals impersonate suppliers, intercept genuine payment conversations or use compromised vendor accounts to request changes to bank details, directly links social engineering to third-party risk.

Taking a step back, the initial harvesting process of personal data for social engineering attacks can be streamlined. AI can automatically scrape data from public sources such as Companies House and social media to quickly provide the names of specific people, their roles and relationships.

When trust and identity come under attack

Even on video conferencing calls, it’s becoming increasingly difficult to tell if the person you’re speaking to is real due to the increasing accuracy of deepfakes. As an example, it’s often now necessary to ask a suspected deepfake to do something it wasn’t programmed to do, such as raise a hand, to check if the person in question is real. But even that test is gradually being circumvented by new technology.

Organizations need stronger out-of-band verification protocols for high-value or unusual requests. A pre-agreed code word via a separate channel might be needed to ensure trust and security.

Identity security is becoming a key area of defense as autonomous attacks become more advanced. Credential stuffing at scale, session cookie harvesting, MFA fatigue attacks and vishing attempts designed to bypass multi-factor authentication are all increasing. AI can make these attacks more efficient by identifying likely targets, generating convincing scripts and adapting to the victim's responses in real time.

This is why identity and access management should be treated as a critical control. Organizations need to know who has access to what, whether that access is still needed, which accounts are privileged and how quickly unusual behavior can be detected.

Fighting AI with AI

AI-driven autonomous attacks might be heightening the risk, but AI can also be used defensively. A good example of this is to run an automated risk analysis of an organization and highlight where security tools and the basics, such as malware protection, are out of date or missing.

With those fundamentals in place, AI can then underpin continuous monitoring of the critical systems, rather than periodic checks. Businesses should be identifying and focusing on protecting the “crown jewels” – that might be the top 10 most critical assets, such as payroll or a banking system, and target AI-led efforts on protecting them.

Joined-up visibility is then crucial. Businesses need to know who has access to those critical assets, the endpoint and network activity related to them and gain the ability to correlate any incidents quickly so the response to an AI-driven attack can be as swift as possible.

A combination of AI-powered technology, backed by human expertise, can provide proactive threat hunting to actively search for, investigate and remediate dangers, even if they are autonomous in origin.

Organizations aren’t powerless in the fight

The rise of autonomous attacks marks a new phase in cyber risk. For many businesses, particularly smaller ones, the challenge is preparing for attacks that can move much faster than traditional defenses. But organizations aren’t powerless in the fight.

Effective responses start with getting the basics right, from access controls to visibility across critical assets, to moving from periodic checks to continuous monitoring and faster detection with AI.

However, technology alone won’t be enough. Human expertise can interpret risk and make informed decisions under pressure to ensure resilience, even as the AI-driven autonomy threat moves to the next level.

We've featured the best firewall software.

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 title and cast of James Gunn's Superman spin-off have been revealed
• The People v. Gorilla Grodd will be a mockumentary series fronted by Jimmy Olsen
• DC fans think it's the perfect project to introduce The Flash to the DCU

James Gunn's Superman spin-off show is officially in development at DC Studios — and its title, cast, and premise suggest it could be the most unusual DC Universe (DCU) project so far.

Nine months after it emerged that a Superman TV off-shoot was in the works, the actual name of a production tentatively titled DC Crime has finally been revealed. It's called The People v. Gorilla Grodd and, as was previously reported, it'll be a mockumentary series that will center on the hyper-intelligent, telepathic ape known as Gorilla Grodd.

Until now, we'd known next to nothing else about the DCU Chapter One TV show. But, with the eight-episode, half-hour crime comedy series officially greenlit by HBO Max, and DCU overlords Gunn and Peter Safran, we have a much clearer picture of what it's about and who'll star in it.

The People v. Gorilla Grodd cast: who's involved? And where's Rachel Brosnahan's Lois Lane?

Extra! Extra! Read all about it! The new DC Studios series The People v. Gorilla Grodd is coming soon to HBO Max. pic.twitter.com/cMfgyhC2j6August 21, 2026

Is James Gunn writing and directing The People v. Gorilla Grodd?

No. Tony Yacenda and Dan Perrault, the creatives behind Netflix crime mockumentary show American Vandal, have been installed as The People v. Gorilla Grodd's showrunners, writers, and directors. They're part of its executive producing team, too, which also includes Gunn and Safran.

Per an HBO Max press release, The People v. Gorilla Grodd will reunite viewers with four characters from last year's Superman movie, all of whom work for Metropolis news outlet The Daily Planet.

Skyler Gisondo's Jimmy Olsen has been installed as the show's protagonist. Meanwhile, Beck Bennett as Steve Lombard, Mikaela Hoover as Cat Grant, and Wendell Pierce as Perry White, all of whom had minor roles in Superman, are also set to feature.

Curiously, Rachel Brosnahan's Lois Lane isn't part of proceedings. Does this have something to do with her role in 2027 Superman sequel Man of Tomorrow? I guess we'll find out at some point.

There are plenty of other familiar faces among the TV project's ensemble. Jimmy Tatro is the most noteworthy of those new DCU additions, with the American Vandal alumnus set to play Grodd.

Mary Holland (Ghost), Eduardo Franco (Stranger Things), Arian Moayed (Wonder Man), Dan Perrault (American Vandal), Andrew Leeds (Zoe's Extraordinary Playlist), and Tim Baltz (The Righteous Gemstones) are also involved, though their roles are being kept under wraps for now.

What is the plot of The People v. Gorilla Grodd?

The forthcoming crime comedy show will examine a potential miscarriage of justice involving Gorilla Grodd (Image credit: DC Comics)

Here's the official logline for The People v. Gorilla Grodd: "A superintelligent ape is convicted of murdering his father, the King of Gorilla City. In an eight-part 'true' crime docuseries, the Daily Planet’s Jimmy Olsen reopens the case, investigating whether Gorilla Grodd was wrongly convicted in one of Metropolis’ most famous murder trials."

But wait, there's more, because the above X/Twitter post gives us a more detailed look at the show's wider narrative.

"The People v. Gorilla Grodd will take a fresh, 360-degree look at the case, revealing the 'lesser-seen side' of the hyper-intelligent ape, according to the film's first-time director Jimmy Olsen," it reads. "Olsen was granted unprecedented, exclusive access to interview Grodd inside Stryker's Island Penitentiary for the documentary.

"Grodd has long claimed his innocence in the murder and brain consumption of his father, King Grodd — a crime he was convicted of in March 2021 and has remained incarcerated for ever since. The series will take an in-depth look at the investigation, featuring brand new reporting from members of the Daily Planet staff, as well as interviews with witnesses and experts on both Grodd's and the Metropolis District Attorney's side of the case, including Grodd's new counsel at the Metropolis Freedom Project."

Will The Flash appear in The People v. Gorilla Grodd?Comment from r/DCULeaks

Nobody knows, though I can see why DC comic book fans are suddenly excited about such a possibility. After all, Gorilla Grodd is one of The Flash's most notorious foes, so it's perfectly understandable that some believe that the speedy metahuman — real name Barry Allen — could make his DCU debut here.

In fact, some fans have already convinced themselves that The Flash will show up in The People v. Gorilla Grodd. Threads on r/DCULeaks and r/DC_Cinematic are sprinkled with such comments, so it'll be interesting to see if the Scarlett Speedster makes an appearance or not whenever the series is released.

Are you excited for The People v. Gorilla Grodd, or would you rather DC Studios focus on introducing Batman and Wonder Woman to the DCU? Let me know in the comments.

And, if you're after more DCU coverage to consume, check out my Sinestro in Lanterns explained and Lanterns' Manhunters explained pieces.

Lorcana has exploded since it came on the scene, and I had a chance to chat with co-founder Ryan Miller about where it may be going next.

If you’re worried about Flock cameras, start by learning to recognize them.

My house contains a small but steadily expanding museum of obsolete technology. I've always been reluctant to get rid of them, even if I don't use them for years at a time, so they just fill up boxes in my attic. I know theoretically they're worth something, but I never feel motivated to actually go through them and see what people on eBay might pay.

But I thought ChatGPT might be able to do all of that for me from a simple photograph. I took a picture of the stuff in one of my boxes. Like many boxes stored in my house, this one had lost any obvious reason for existing. It contained a Nintendo Wii, two games, an elderly Kindle, and several DVDs that had survived multiple clear-outs.

I asked ChatGPT to identify everything, determine what might still have resale value, and check recent eBay sales to find out how much it might be worth. I specifically told ChatGPT to look at completed or sold listings, not merely the prices sellers were asking. Anyone can list a dusty iPod for $500 and describe it as rare; I wanted to know what a buyer actually paid.

Wiis and DVDs

(Image credit: Nintendo)

ChatGPT had little trouble recognizing the white Nintendo Wii and the familiar Wii Sports sleeve. It also identified the second game as Marvel: Ultimate Alliance 2, although it asked for a closer photograph to confirm that the disc and manual were inside.

The DVDs were identified from their spines, but ChatGPT warned that ordinary used movies generally have very little individual resale value. It suggested checking for out-of-print titles, complete television series, unusual editions and sealed copies before assuming they belonged in a bulk lot.

The Kindle in the box turned out to be a first-generation Kindle Fire rather than one of Amazon’s E Ink readers. ChatGPT identified it from its thick black body and confirmed the model. It still worked, though the resale results were not especially exciting. Working first-generation Kindle Fires generally appear on eBay for about $10 to $20. Current eBay comparisons suggest a realistic selling price of about $15 to $20 for a tested, reset unit in decent condition.

A tidy sum and cleared space in my attic

The Wii powered up without complaint, which was more than I expected after its extended sabbatical. The disc drive worked, the remote connected and the console reached its home screen without producing any alarming noises. Recent white Wii sales put the market value of the console alone at approximately $40, with complete packages commanding more.

ChatGPT estimated the hardware at approximately $50 to $60. The more interesting discovery was Wii Sports. Since the game came with millions of consoles, I had assumed it was practically packaging material. Recent eBay transactions suggested otherwise. The current market is close to $30, almost as much as the system that plays it. People buying replacement consoles often want the game they remember playing with them, usually before someone put the remote through a television.

All told, ChatGPT said I might get between $115 and $135 for the complete box. The precise total would depend on condition, postage, selling fees, and whether buyers made lower offers. It would also require me to photograph everything properly, write accurate listings, and take seven packages to the post office. After a couple of days on the website, I managed to get pretty much what ChatGPT estimated, with $130 dollars for the lot.

The experiment worked because ChatGPT did more than name the objects. It asked for model numbers, checked whether accessories were present, distinguished complete games from loose discs, and noticed when an individual item deserved its own listing. It also prevented the DVDs from acquiring imaginary value merely because they had become old. It may not be the Antiques Roadshow retirement fund, but it's enough to encourage me to take more photos of the boxes in my attic this week.

Commerce in the information age has thus far been one long quest to gather as much information as possible. Customer data, sales data, inventory data, system data, operational data, technical data, every facet of business can and has been tracked and quantified, guided by the idea that knowing more means decisions can be made faster and more conclusively.

While sound in theory, that process has clearly hit a snag, as more and more companies abandon their AI projects after finding that the exponential explosion of data creation isn’t generating comparable value.

Has AI peaked in its utility? Or is the issue one of interoperability? Why exactly has this monumental trend hit such a stumbling block?

Expecting scale to produce clarity

It’s entirely understandable to assume that feeding more information into an AI increases the accuracy and quality of the output. Models need to train on data; after all, that is how they establish reason.

But, like any computer system, input must be structured for it to be understood, and the issue many organizations now find themselves in has come as a result of them giving AI a decade's worth of unformatted, incomplete, non-standardized data and expecting it to read between the lines.

Every system a company uses, from CRMs and internal emails to performance tracking spreadsheets and PDFs, stores different data points and operates on vastly different logic. What they host, why they host it, and how they host it were all purposeful decisions based on their desired function.

While some support import and export options that translate material from one format into another, the developers behind these solutions never envisioned the need for a universal logic to tie everything together in a way that AI can easily parse.

Each document and dataset contains a fragment of the overall picture. While AI excels at processing data, it cannot derive meaning and struggles to understand and incorporate unstructured data into its output, regardless of how many times it is asked to.

An example of fragmentation in action

We have largely solved the problem of data accessibility to the point that many public AI models have run out of new data and are now cannibalizing the output of other agents. The next step is in refining how AIs use the data they have.

Real estate is an excellent showcase of this process in motion. To appraise and list a property, agents need access to ownership records, zoning information, environmental information, and neighborhood demographics, just to name a few. None of these systems was designed to communicate or cooperate, which has meant AI has historically struggled to find its footing in the industry.

Newer approaches, however, prioritize the ability to find, interpret, and synthesize information from across different sources, aiding in the manual searches an analyst would otherwise do by hand.

Data siloing and fragmentation slow down decision-making, and any systems that can account for them will command a premium going forward.

Every industry has its own version of the same problem: a wealth of data at its disposal and no meaningful way to turn it into actionable insight.

A generic, trend-inspired adoption of AI tools won’t necessarily solve these underlying issues, so it’s ultimately no surprise that many are abandoning their projects altogether. It will take models that prioritize context and connection, and companies paying more attention to how they store and format data, to address the glut in productivity and decision-driving insight AI is currently experiencing.

Solving information overload

AI and the challenges it now faces are a classic example of the dangers of prioritizing quantity over quality. To be entirely fair to users, the companies behind these agents share some blame for this predicament.

The technology is still in its infancy, and marketing hype continues to push scale and processing power as key features, while the much more valuable aspects, like contextual understanding and data integration, fly under the radar.

This discrepancy will likely shift as more success stories highlight the competitive advantages of automating manual, time-consuming processes, as the real estate industry has with property research. It is this ability to understand what exists at a deeper, more comprehensive level that yields the greatest decision-supporting insight.

We've featured the best AI website builder.

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

IndyCar drivers zoomed past some of the nation's most famous landmarks this weekend. They faced novel challenges on an unconventional route.

(Image credit: Nathan Howard)

As President Trump's deployment of the National Guard to Washington, D.C. enters its second year, three guard members who served many months on the mission share their reactions with NPR.

(Image credit: Illustration by Mari Fouz​ for NPR)

Sean Grayson, a former Illinois sheriff's deputy whose fatal shooting of Sonya Massey inside her home became a flashpoint over police use of force, died Sunday while serving a 20-year prison sentence.

(Image credit: Illinois State Police)

A seamless airport journey can feel effortless. From check-in to baggage handling and take-off, everything appears coordinated, predictable and, for the most part, smooth. When delivered without any hitches, the entire process feels remarkably simple.

But, beneath that simplicity sits one of the most complex operational environments imaginable. Airlines, baggage handlers, security teams, retailers, air traffic control and countless other stakeholders must all coordinate in real time to keep passengers and aircraft on the way to their next destination.

Most travelers never see this complexity, but without it the entire experience would quickly fall apart.

I’ve long been fascinated by the inner workings of airports. As the son of an air traffic controller, I grew up with an appreciation for aircraft and the decisions that are made every minute to make air travel possible. Now, I find myself regularly drawing parallels between airport operations and the way businesses are approaching their AI deployments.

What I have realized is, the most valuable use of AI may not be the highly visible applications that are drawing headlines and changing the way we’re searching. Instead, its greatest impact could come from helping businesses manage the complexity of their operations behind the scenes.

The illusion of simplicity

Airports are designed to feel intuitive and like they just ‘work’, but that simplicity is very carefully engineered by experts with decades of experience. Every stage of the passenger journey depends on hundreds of connected decisions, working together and pivoting when needed.

Aircraft availability, staffing levels, security capacity, weather conditions and passenger demand must all be monitored and coordinated continuously. And while many of the systems supporting these individual activities were never originally designed to work together, they have no choice but to operate as one.

Businesses nowadays face a similar challenge. What appears seamless to a customer often relies on a web of technologies, data sources and processes operating in the background. Orders, supply chains, finance, procurement and workforce management all generate information that must be interpreted and acted upon almost instantly.

When these systems are disconnected, organizations create inefficiencies, blind spots and unnecessary friction that can have a knock on impact on the end customer.

This is where AI has the potential to become transformative. The challenge is no longer simply connecting systems that don’t traditionally work together. Now, it is about understanding what the data flowing through those systems means and determining the best next step.

By identifying patterns, discovering insights and supporting decisions, AI can help businesses turn operational complexity into coordinated execution.

Responding to constant change

The comparison with airports becomes even more prevalent when operations start deviating away from the original plan. The major travel hubs with the best reputations are not those that have every flight departing exactly as scheduled, they are the ones that are able to quicky respond when conditions change.

Flights are delayed, weather disrupts operations and passengers miss their connections, but the system always seems capable of adapting. Gates are reassigned, baggage is rerouted and resources are redirected, often before passengers even notice that there is a problem.

This level of responsiveness depends on more than just connected systems. It requires the ability to interpret information and act on it quickly. Increasingly, AI is helping businesses do exactly that by analyzing data and signals in real time, anticipating potential disruptions and recommending the next best course of action.

Customers rarely see this complexity. They expect products to be available, services to be reliable and experiences to be personalized, and they’re not massively bothered by the mechanics behind this.

At the same time, organizations are operating in an environment that is defined by constant disruption, from shifting consumer demand and supply chain pressures to economic uncertainty and regulatory change.

Static processes are struggling to keep pace with that reality and as a result, businesses are moving towards more adaptive operating models where workflows can respond to changing conditions and issues before they become critical problems. The benefit is not simply greater efficiency. It is resilience.

From coordination to intelligence

Looking ahead, the next evolution for both airports and businesses is undoubtedly going to be driven by prediction rather than coordination. Airports are already exploring how data and AI tools can help anticipate passenger congestion, optimize security flows and improve the overall travel experience.

Businesses want to follow a similar path. Early AI initiatives have, for the most part, focused on individual tasks and use cases, but the tide is starting to turn. Now, organizations are looking at how intelligence can be embedded directly into their core processes, breaking free from pilots (pun intended!) to full scale autonomous operations.

The result is a shift from fixed processes to ones that are far more intelligent. Instead of simply automating individual tasks, businesses are now understanding how they can create processes that improve over time, helping people make better decisions and respond more effectively to changing circumstances.

The smoothness of an airport journey is rarely an accident. It is the outcome of a multitude of carefully integrated technologies working together to manage the complexity of air travel behind the scenes. As businesses grow, build new capabilities and onboard more team members, they face the same challenge: delivering what looks like simplicity on the surface while managing growing complexity underneath.

Success will depend not only on connecting systems and consolidating data, but on embedding intelligence into the very fabric of operations. The organizations that achieve this, will be those that are best equipped to anticipate challenges, adapt to change and continuously improve.

Ultimately, customers may never see the work that goes on behind the scenes to drive an experience. Just as travelers rarely think about the systems that keep an airport running, they are unlikely to notice the technology enabling a faster delivery, a smoother transaction or a better service experience. What they will notice though, is when everything just works.

We've featured the best AI chatbot for business.

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

Treasury secretary to detail new round of economic pressure on Iran, Iran warns of 'seismic' retaliation if U.S. follow through with economic threat, Canada vows to impose retaliatory tariffs on U.S.

Companies have received tariff refunds. What about consumers? Some companies are providing refunds while others decline.