How do teams know whether an AI agent action is suspicious or just authorised?

Why This Matters for Security Teams

Suspicious AI agent behaviour is hard to judge because authorised autonomy can look like abuse when it is viewed one event at a time. A database read, API call, or ticket update may be perfectly legitimate, but the same action becomes concerning if it appears in a new sequence, outside the expected task window, or with broader data access than normal. Guidance in the NIST AI Risk Management Framework reinforces the need to manage AI risk as a system property, not a single-alert problem.

For security teams, the real question is whether the action matches the agent’s approved mission, identity, tool permissions, and recent behaviour. That means the baseline must reflect the specific agent, its role, and the environment it operates in. A marketing support agent and a production change agent should never be judged against the same pattern of normal activity. In practice, teams that rely only on static allowlists often miss compromise because attackers stay within permitted tools while shifting timing, volume, or sequence. In practice, many security teams encounter the difference between authorised and suspicious behaviour only after an agent has already expanded its scope, rather than through intentional behavioural monitoring.

How It Works in Practice

Teams usually need three layers of context to decide whether an action is suspicious: identity context, task context, and behaviour context. Identity context asks which agent instance, credential, or service identity performed the action. Task context asks whether the action fits the current job, policy, or workflow state. Behaviour context asks whether the action is consistent with the agent’s historical pattern. This is the practical shift recommended by current guidance from the OWASP Agentic AI Top 10 and the MITRE ATLAS adversarial AI threat matrix: do not treat one tool call as the event, treat the full execution path as the signal.

A practical detection model often includes:

• baseline permissions for each agent identity, including tools, data sets, and downstream systems
• expected tool sequences for each workflow, such as retrieve, reason, draft, then submit
• rate and volume thresholds that reflect the agent’s normal operating rhythm
• change detection for new destinations, elevated scopes, unusual retries, or data exfiltration patterns
• policy checks for whether human approval is required before a high-impact action

Security operations teams should correlate agent telemetry with access logs, API gateway logs, prompt and tool traces where appropriate, and incident workflows. The aim is not to block every deviation, because some deviations are legitimate during task changes or model updates. The aim is to score deviations against mission context and escalation rules. The agent should be able to act freely inside its approved envelope, but anything that widens scope, changes sequence, or crosses a sensitivity boundary should trigger review. This becomes especially important in environments that use shared credentials, loosely scoped service accounts, or incomplete tool logging, because the signal disappears before analysts can determine whether the action was authorised or malicious.

Common Variations and Edge Cases

Tighter detection often increases operational overhead, requiring organisations to balance faster incident spotting against false positives and analyst fatigue. That tradeoff is real, especially when agents are still being tuned, because legitimate behaviour may change as prompts, tools, or business workflows evolve. Best practice is evolving here, and there is no universal standard for how much deviation should be tolerated before escalation.

Edge cases matter. An agent may appear suspicious because it queries an unfamiliar database, but the action may be authorised if the task was reassigned mid-flight. A burst of outbound calls may be normal for one batch process and abnormal for another. Shared runtime identities are particularly difficult because the baseline becomes blurry and attribution is weak. Where organisations rely on autonomous agents for privileged tasks, current guidance suggests pairing behavioural monitoring with explicit approval gates for sensitive actions, rather than assuming the baseline alone will separate normal from malicious behaviour.

That is where the intersection with NHI governance becomes important: if the agent identity is not strongly bound to its permissions, workload, and owner, the team cannot tell whether the behaviour is unexpected or simply poorly governed. The strongest control is usually not a single detector but a combination of least privilege, step-up approval, and traceable execution records. For practical implementation, security teams should also align their logging and control design with NIST SP 800-53 Rev 5 Security and Privacy Controls and the CSA MAESTRO agentic AI threat modeling framework where agent workflows include privileged or chained actions.

Related resources from NHI Mgmt Group

• How do security teams know whether an AI agent is operating safely?
• How do teams know whether an AI agent audit trail is complete enough?
• How should security teams decide whether an AI agent gets human or non-human identity?
• How do teams know whether AI-assisted IGA is actually working?