How can security teams tell normal AI agent activity from misuse?

Why This Matters for Security Teams

Normal-looking agent traffic can become misuse very quickly because AI agents do not behave like static service accounts. They chain tools, retry failed calls, expand scope when blocked, and may touch data or destinations that were not part of the original request. That makes simple allowlists and single-event approvals weak indicators of safety. The practical question is not whether an agent was authorised once, but whether its current behaviour still matches the purpose it was given. Current guidance in the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point toward runtime evaluation, not one-time trust decisions. NHIMG research on the State of Non-Human Identity Security shows only 1.5 out of 10 organisations are highly confident in securing NHIs, which is a warning sign for anyone relying on coarse monitoring alone. In practice, many security teams discover abnormal agent activity only after an API key is abused, a workflow is chained into an unintended action, or a tool call has already caused impact.

How It Works in Practice

Security teams should detect misuse by baselining each agent as a workload identity, not as a generic user. That means tracking per-agent patterns for API frequency, typical destinations, payload size, time-of-day, tool chain depth, and process behaviour. The baseline should be tied to the task class and the environment, then compared continuously against live activity. A procurement agent that normally reads invoices and updates one system should not suddenly enumerate storage buckets, call admin endpoints, or emit large bursts of token requests. This is where intent-based or context-aware authorisation becomes important. Rather than asking whether the agent is “allowed” in the abstract, policy engines evaluate what the agent is trying to do right now, with what data, from which context, and through which tool. That is closer to the direction described in CSA MAESTRO agentic AI threat modeling framework and the NIST AI RMF’s governance and mapping functions. Implementation usually combines:

• short-lived workload credentials instead of long-lived secrets,
• policy-as-code checks at request time,
• tool-level logging with full prompt, action, and destination context,
• step-up controls when behaviour deviates from the agent’s normal sequence,
• automatic revocation when a task completes or a threshold is crossed.

For practical detection work, NHIMG’s analysis of the OWASP NHI Top 10 is especially relevant because it frames agent misuse as a control problem, not just a logging problem. These controls tend to break down in highly autonomous multi-agent systems because one agent’s “normal” tool chain may be another agent’s lateral movement path.

Common Variations and Edge Cases

Tighter detection often increases operational overhead, requiring organisations to balance precision against noise and response speed. There is no universal standard for agent baselining yet, so current guidance suggests tuning by mission criticality rather than enforcing one flat threshold everywhere. Low-risk assistants may tolerate broader ranges, while agents with write access, financial authority, or production tooling need much narrower tolerances. Edge cases matter. A burst of activity may be normal if an agent is completing a backlog job, but the same burst is suspicious if it arrives after a new prompt, a failed tool call, or an unexpected data source. Likewise, agents that operate across SaaS, internal APIs, and code execution environments can look benign in each individual system while still forming a risky sequence overall. That is why the best signal comes from sequence and context, not isolated events. Security teams should also watch for model behaviour drift. A change in prompt design, tool availability, or retrieval source can alter the agent’s baseline without any malicious intent. The NIST AI Risk Management Framework is useful here because it reinforces ongoing measurement rather than static approval. And because agent misuse often starts with identity compromise, NHIMG’s State of Secrets in AppSec is a reminder that weak secret handling can make “normal” agent activity indistinguishable from abuse until damage is already done.

Related resources from NHI Mgmt Group

• How should security teams monitor AI agent activity without disrupting developers?
• How should security teams handle AI agent visibility?
• How can security teams tell whether an agent tool surface is too narrow?
• How should security teams govern machine identity credentials in agentic AI environments?