What should organisations do when AI agent behaviour becomes suspicious?

Why This Matters for Security Teams

Suspicious agent behaviour is not just an application anomaly. It is a live identity and authorization problem because autonomous agents can chain tools, change objectives, and move data faster than a human can review a ticket. Static role-based access often assumes predictable paths; agents do not behave predictably. Current guidance in OWASP Agentic AI Top 10 and the AI Agents: The New Attack Surface report both point to the same operational reality: once an agent drifts, the blast radius can expand inside the session before teams notice.

That is why containment should happen at the identity layer, not only at the endpoint or network layer. If the agent is using long-lived secrets, overbroad permissions, or opaque tool access, incident response becomes slower and forensic confidence drops. NHIMG has also documented how agentic systems can cross intended scope and expose sensitive data, which makes behaviour monitoring as important as detection. In practice, many security teams encounter the problem only after the agent has already accessed inappropriate systems or moved sensitive data, rather than through intentional testing.

How It Works in Practice

The safest response is to freeze the agent’s active session, revoke or quarantine its workload identity, and review the full action chain before deciding whether the behaviour was malicious, misconfigured, or simply unexpected. For agentic systems, that means looking at destination changes, volume spikes, new tool calls, and scope expansion as indicators of intent drift. The NIST AI Risk Management Framework is useful here because it frames AI governance as continuous risk monitoring rather than one-time approval.

Operationally, teams should prefer ephemeral credentials, short TTL secrets, and runtime policy checks over static entitlements. This is where OWASP NHI Top 10 and the CSA MAESTRO agentic AI threat modeling framework align well with practice: use workload identity to prove what the agent is, then apply policy-as-code to decide what it may do right now. That usually means:

• Isolate the agent from downstream tools and external connectors immediately.
• Revoke session tokens, API keys, and other secrets tied to the task.
• Compare the action sequence against the agent’s normal task envelope.
• Check whether the agent crossed data boundaries, changed destination, or increased volume.
• Preserve logs and tool traces for reconstruction before resetting access.

This approach is most effective when identities, secrets, and logs are centralized. These controls tend to break down when agents share credentials, operate across fragmented toolchains, or execute through unmanaged plugins because the action trail becomes incomplete.

Common Variations and Edge Cases

Tighter containment often increases operational friction, requiring organisations to balance service continuity against the risk of allowing an agent to keep acting. That tradeoff is especially visible in customer-facing automations and developer copilots, where a full stop can interrupt business processes. Best practice is evolving, but there is no universal standard for when a drifted agent should be paused versus terminated, so incident playbooks should define thresholds in advance.

Edge cases matter. A burst of legitimate activity can look suspicious if the agent has been assigned a complex, one-off task. Conversely, a subtle prompt-injection or connector abuse may look normal until the agent begins changing destinations or exfiltrating data. NHIMG coverage of incidents such as the CoPhish OAuth Token Theft via Copilot Studio shows why teams should treat task context, not just volume, as a control signal. External threat research such as the Anthropic report on AI-orchestrated cyber espionage also reinforces that autonomous systems can be manipulated into broader abuse chains. Organisations should therefore define exception handling, manual approval paths, and emergency revocation steps before the next suspicious session begins.

Related resources from NHI Mgmt Group

• When should organisations treat an AI agent as a privileged system?
• What is the difference between human identity governance and AI agent governance?
• When does AI agent access create more risk than it reduces?
• What is the difference between governing human access and governing AI agent access?