From alert fatigue to autopilot fatigue: How agentic AI shifts cyber risk

Date:
Tue, 23 Jun 2026 14:54:53 +0000

Description:
Agentic AI is reshaping how security teams need to think about risk.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter For a long time, security teams have been dealing with the same problem: a constant stream of security
alerts, but not enough context.

Missing details like user behavior, asset importance, or related activity, means theres a heavy reliance on analysts to work out what actually matters. This doesnt just slow teams down; it puts real pressure on teams and limits how much they can realistically review or understand. Latest Videos From
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Agentic AI changes this dynamic. Andy Fielder Social Links Navigation

Chief Technology Officer, MetaCompliance. Instead of looking at alerts in isolation, these systems can piece activity together, understand whats happening in context, and in some cases take action on their own. You may
like Maintaining cyber control when AI can act autonomously Why security leaders are cautious about agentic AI Why self-running agents are creating
the biggest security crisis of 2026

Often, issues are resolved before they ever need to be escalated. That
removes a lot of the manual effort thats shaped security operations for
years.

But while a clear improvement, it doesnt remove riskit shifts it. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting
your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. As systems improve, scrutiny declines A useful
comparison is aviation. As systems become more reliable, people naturally
step back. Not because theyre careless, but because constantly
double-checking something thats almost always right starts to feel unnecessary. Over time, trust stops being something you actively think about and becomes something you assume.

The same thing is starting to happen in cybersecurity . As these systems
prove themselves, teams spend less time questioning individual decisions. The environment feels calmer, and the lack of issues reinforces that sense of control. The real risk isnt frequent failure, its that when something does go wrong, its less likely to be challenged.

Alert fatigue comes from having to pay attention to too much, too quickly. What follows is something different: a gradual drop in attention, where growing confidence in the system weakens the instinct to double-check. What
to read next How AI agents are wrecking havoc in legacy security setups and enterprises are catching up What the OpenClaw vulnerability reveals about the future of agentic AI security AI agents are the new unmanaged endpoints A model built on two interdependent layers The structure of security operations starts to shift as well. Instead of everything hinging on human decision-making, you end up with two connected layers. People set the intent defining policy, access and boundaries while agents interpret it and act on it, often much faster than any person could.

Both layers can be influenced. Traditional attacks aimed at people dont go away, but theres now another surface to think about: the data, prompts, and workflows that shape agent behavior. If those inputs are manipulated, the system can still produce actions that look valid, because they follow its internal logic.

At the same time, the distance between decision and execution increases.
Human operators arent as involved in the moment an action happens, which
makes it harder to spot when something isnt quite right. In practice, each layer ends up relying on the other for validation.

When that assumption holds, the system works efficiently. When that works, everything runs smoothly. When it doesnt, the gap between them can be hard to see in real time. How risk scales in an agentic environment Risk doesnt just increase in this kind of environment, it spreads differently. Each agent has its own identity, permissions, and decision-making logic, and theyre often connected. Actions taken in one part of the system can trigger responses elsewhere, creating chains of automated behavior.

That means a single bad input or flawed decision doesnt stay contained. It
can move quickly across systems without anyone stepping in. The issue isnt just speed, its how connected everything is. Small mistakes can have much bigger consequences because theyre carried through multiple layers of automation. Why identity and access need to change How agents are set up
today adds another layer of risk. In many cases, theyre treated as extensions of the user, with the same credentials and access. Its convenient, but it
also widens the blast radius if something goes wrong.

A more resilient approach is to treat agents as their own entities. Give them distinct identities, limit what they can do to specific tasks, and make sure their actions can be tracked and reversed if needed, without affecting everything else.

Its less about efficiency and more about putting the right foundations in place for systems that are increasingly acting on their own. Maintaining control as reliance increases One of the trickier aspects is that failure doesnt always look like failure. Fewer alerts and faster resolutions can make it feel like risk has gone down, when in reality oversight may just be less active.

Staying in control comes down to how these systems are designed and used. High-impact actions still need some form of verification, even if most
routine work doesnt. It also matters that teams can see not just what an
agent did, but how it arrived therewhat inputs it used and how it interpreted them.

The ability to step in is just as important. If stopping or overriding an automated process is slow or awkward, it probably wont happen in time when something goes wrong. That kind of intervention needs to be simple enough to use under pressure.

More broadly, the role of the security professional shifts. Its not just
about spotting obvious problems anymore, but recognizing when something that looks fine might still need a second look. A quieter, more concentrated risk Agentic AI will do a lot to reduce alert fatigue, which has weighed on security teams for years. The trade-off is that risk becomes less visible and more concentrated in the space between what people intend and what machines actually do.

In systems that work correctly most of the time, the real challenge isnt constant failure. Its what happens when something does go wrong and whether the usual signals that would catch it are still there. We've reviewed, rated, and ranked 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



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