AI Psychosis: When trust in machines clouds human judgment

By Huzefa Motiwala, Senior Director, Technical Solutions, India and SAARC, Palo Alto Networks

‘AI psychosis’ is easy to mistake for a flaw in the machine; like something is broken in the algorithm, as if the model itself were drifting from reality. In truth, it points to a flaw in us; in how quickly we let our own judgment bend around the machine’s outputs. We’ve seen versions of this story before. Pilots have followed autopilot systems into disaster. Traders have trusted black-box models until markets collapsed. Now, as AI threads into security operations, business decisions, and government systems, the risk of blind trust is surfacing again.

When small errors scale into systemic failures
In India especially, AI already underpins financial fraud detection, welfare distribution, citizen services, and even defense-adjacent systems. This scale raises the stakes and it’s where AI psychosis matters most; not in the abstract, but in the everyday. An algorithm wrongly declining a single welfare payment could be a one-off error. An algorithm wrongly declining ten million because no one questioned its output is an institutional breakdown.

When public trust is eroded by machine errors that go unchallenged, the consequences are not technical but societal.

Dashboards are gospel… until they are not
In security operations centers specifically, AI-powered dashboards give the illusion of certainty: alerts neatly stacked, anomalies color-coded, priorities ranked. It feels decisive, it feels comfortable. But AI doesn’t deal in absolutes. It detects patterns, and patterns often deceive.

The real danger here is automation bias: the tendency to believe the machine knows best. Under pressure, even skilled analysts can overweight an algorithm’s call, dismissing their own instincts. Over time, the very skepticism that prevents mistakes starts to dull. The risk isn’t that AI hallucinates. It’s that humans hallucinate AI’s authority.

Adversaries are playing this game too
Attackers understand this dynamic and are already learning how to exploit it. They can flood SOCs with low-grade alerts, betting that the real threat slips through. They use voice cloning to bypass human verification, prompt manipulation to nudge models into misclassifying threats, and SEO-poisoned sites to trigger user-initiated compromise.

Just as phishing emails once preyed on human trust, adversaries are now targeting machine trust, and by extension, the humans who rely on it. In 2025, Unit 42 observed threat actors using generative AI to craft tailored lures, clone executive voices, and sustain live engagement in impersonation campaigns. The attack surface is now psychological too.

The first crack is always human
Every breach story still begins with a person or a process: a hurried click, a help desk reset approved too quickly, or an account with far more privileges than it needs. Unit 42 data shows that 36% of all incidents between May 2024 and May 2025 began with social engineering. Not zero-days. Not cutting-edge malware. Human decisions. And in 60% of those cases, the result was data exposure.

AI helps erase a lot of these weak links, but in fringe cases, it can amplify them. A model automating account resets can make a single misclassification scale across thousands of users. An oversensitive AI-driven fraud detection system making false positives can freeze not one account, but millions. In security, the first compromise is almost always human, and AI can accelerate both the cause and the consequence.

Sanity checks, not silver bullets
The threats may be novel, but the countermeasures aren’t. Expose the raw evidence behind every AI-driven alert. Display confidence ranges instead of binary “yes/no” answers. Require human review for high-stakes actions like policy changes or credential resets. And stress-test AI systems just as we red-team networks.

It’s not so much about distrusting machines; more about keeping human judgment sharp. AI will make mistakes, and it’s our job to catch them.

The way forward
The safest path forward is AI-accelerated yet human-anchored. Machines can cut through noise, surface weak signals, and scale response. But people preserve context, accountability, and the ability to ask: Does this make sense?

It’s simple to hand over the keys to the machine and call it a day. But organizations that resist this temptation and keep humans firmly in the loop, questioning, challenging, and course-correcting will flourish in this new world order.