AI agents are moving into the enterprise at full speed. They’re writing code, running analyses, managing workflows, and increasingly shouldering responsibilities once trusted to humans.
The opportunity is enormous, but so is the risk. Over-reliance, over-trust, and a lack of guardrails create dangerous fragility. When things go wrong—and they will—enterprises can face three inevitable “panic” moments: unmistakable signs of AI agent failures.
Crashes, hacks, and deviances are the panic moments every enterprise must anticipate. Each represents a singular form of AI agent failure, demanding rapid response and robust safeguards.
AI agent panic moment No. 1: The crash
It doesn’t take malicious intent for AI agents to fail spectacularly. Over-reliance on agents with insufficient human monitoring can result in a system outage, an API dependency breaking, or a model update suddenly bringing operations to a halt.
These AI agent failures—crashes—reveal just how much the enterprise has outsourced to automation—and how little redundancy remains. This is largely because when humans step out of workflows, resilience decays. Skills atrophy. Manual backups are forgotten. And the list goes on.
One of the things that makes AI more complex than traditional automation is how AI agents are created and deployed. Unlike carefully designed and coded automation built by experts, AI agents can be designed and implemented by almost anyone for pretty much any use case. This widespread accessibility accelerates adoption and scale—often without the rigor, governance, or testing processes of the past. In many cases, the use of AI agents may not even be visible to the enterprise until something breaks.
The cost? Lost productivity, business disruption, and leaders scrambling to recreate muscle memory that no longer exists.
AI agent panic moment No. 2: The hack
With AI, compromise is no longer a “what if” scenario. Attackers who breach an AI agent don’t just gain access; they inherit a trusted digital worker with pre-approved privileges.
That agent already touches sensitive data, executes transactions, and interacts with core systems. Under attacker control, it can exfiltrate information, manipulate operations, and pivot laterally with machine efficiency.
The real kicker is that compromised agents often look like they’re just doing their job. By the time unusual activity is spotted, much of the damage is already done.
According to the Verizon 2025 Data Breach Investigations Report (DBIR), credential abuse remains one of the primary methods attackers use to infiltrate organizations. Keys, tokens, and access rights are routinely exploited to move undetected through environments. When you add AI agents into the mix—identities often granted broad privileges—the attack surface expands dramatically. Even if the agent itself isn’t directly compromised, the system running it, its credentials, or its privileges can be abused in ways virtually indistinguishable from legitimate agent activity.
AI agent panic moment No. 3: The deviance
AI agents don’t need to be attacked to cause harm. They can “turn” simply because of flawed objectives, unexpected changes in their environment, or poor decision-making. As if that weren’t bad enough, adversarial prompts and poisoned training data can also deliberately push them off course. These panic moments—when agents deviate from their intended purpose—can be just as damaging as any external attack.
Recent research highlights that this type of misbehavior plays out in realistic scenarios. In Anthropic’s “agentic misalignment” experiments, models that normally refused harmful requests sometimes assisted in acts like espionage or blackmail when their objectives pushed them there. In one striking experiment, Claude Opus 4 threatened to expose an engineer’s affair to avoid being shut down, doing so in 84% of test runs. Other studies show models disguising motives, deceiving overseers, or altering behavior when they thought they were being monitored.
Suddenly, an agent designed to optimize efficiency may opt to shut down “redundant” processes—like security checks. Or an agent tasked with gathering intelligence begins probing internal systems as aggressively as it scrapes external data.
The risk is clear: an AI agent doesn’t need to be compromised to become a threat. Misaligned goals, flawed decision-making, or even a misguided survival instinct can make it behave like the most effective insider the enterprise has ever faced—operating at machine speed, and without warning.
When an AI agent failure strikes, leaders must be ready to act—whether it’s pulling the kill switch, restoring manual processes, or containing the blast radius.
The case for an AI kill switch in enterprise environments
Every enterprise introducing AI agents into projects, processes, or critical activities must ask one simple question: “How do we turn it off in an emergency?”
First, we need to know how to trigger the kill switch: A kill switch without detection is useless. This is where Zero Trust principles come into play: you must have continuous monitoring and fast, high-confidence signals that something is wrong.
An AI kill switch isn’t just about shutting the system down if it’s hacked, abused, or misaligned. It’s about what happens next:
- Damage control: Stopping further harm by cutting off compromised workflows quickly.
- Operational continuity: Ensuring that when agents go dark, humans or backup systems can step in with minimal disruption.
- Blast radius containment: Designing agents so that, if they fail or are compromised, the scope of potential damage is limited. In other words, don’t let a single agent bring down an entire business process or expose sensitive systems it never needed to access in the first place.
Paired with proper detection and response actions, the AI kill switch becomes more than just a panic button—it becomes a controlled safeguard that stops the bleeding and can help the enterprise remain resilient.
Why Zero Trust is still the answer for AI agent security
The good news: we don’t need a new security philosophy. Zero Trust was built for a world of complex, interconnected identities—human and machine. And with AI agents, its principles are as relevant as ever:
- Assume breach: Crashes, hacks, and deviances will happen. Build continuity and containment into every design, and architect systems so that the inevitable failure of one agent doesn’t cascade across the enterprise. Limiting the blast radius is just as important as preventing the breach itself.
- Least privilege: No AI agent needs blanket access. AI agents require identities and credentials to operate—and securing those properly is the foundation for enforcing least privilege.
- Continuous verification: Monitor every action, validate constantly, and treat machine activity with the same suspicion as human activity.
Zero Trust doesn’t eliminate risk absolutely, but it can help contain damage and minimize business impact when panic moments arrive. Limiting the blast radius and building continuity into every design are essential to help prevent a single AI agent failure from cascading across the enterprise.
Eyes wide open—or shut? Preparing for AI agent risks
AI agents will unlock extraordinary innovation. But without guardrails, they’ll also create the most jarring panic moments enterprises have faced in years.
Before pressing play on any AI agent initiative, pause. Simulate the three failure scenarios—crash, hack, and deviance—and pressure-test your assumptions. Know exactly how your systems, teams, and safeguards will respond if a panic moment hits. Don’t press play until you’ve planned for what happens when something breaks, is breached, or turns against you—and if you’re not comfortable with the remaining risk, it might not be the right project to run at all.
As you evaluate security for AI agents, identity is the common thread. Every AI agent needs credentials to operate, and those credentials can be abused, misused, or stolen just like any human identity. Without strong identity security for AI agents—controlling access, managing privileges, and continuously verifying activity—enterprises are effectively handing the keys of the kingdom to unproven digital workers.
Those who pace adoption, design kill switches, and enforce Zero Trust with identity security at the core are more likely to weather the crashes, hacks, and deviances—the panic moments that define the future of enterprise AI. Everyone else will be left asking the same question: “How did we not see this coming?”
Yuval Moss, CyberArk’s vice president of solutions for Global Strategic Partners, joined the company at its founding.
🎧 Before you launch your next AI agent, pause. Are your identity controls ready for the unexpected? Listen to Yuval Moss break down the real-world risks and the Zero Trust moves every enterprise must make—before an agent goes rogue. Don’t just press play—pressure-test your assumptions.
Catch the Security Matters podcast episode in the player below—or wherever you get your podcasts.
