If an AI agent can choose actions, call tools, or move between systems during runtime, it should be governed as a distinct identity class with explicit policy and audit coverage. If it is just a scripted workflow, ordinary machine identity controls may be enough. The decision should follow behaviour, not the label attached to the system.
Why This Matters for Security Teams
The decision is less about where the control lives and more about what the system can do at runtime. If an AI agent can choose actions, invoke tools, or chain across systems, it behaves like a distinct non-human identity with its own blast radius. That is why guidance in OWASP NHI Top 10 and the OWASP Agentic AI Top 10 increasingly treats agent behaviour as a governance boundary, not just an authentication event.
Traditional IAM works well when access patterns are stable, human-defined, and easy to review. It breaks down when the workload is goal-driven and can vary tool selection, data access, and execution path from one task to the next. That is why current guidance suggests evaluating the autonomy of the workload first, then deciding whether it belongs under ordinary machine identity controls or in a separate governance model with explicit policy, audit, and containment. In practice, many security teams discover the mismatch only after an agent has already touched systems no one expected it to reach.
How It Works in Practice
A practical decision model starts with runtime behaviour. If the system only runs a fixed script, uses one service account, and cannot make independent choices, standard IAM, PAM, and service-account hygiene may be sufficient. If the system can select tools, branch logic, call external APIs, or move across environments based on prompts or model output, it should be treated as an ai agent identity with separate controls. That distinction aligns with the NIST AI Risk Management Framework and the CSA MAESTRO agentic AI threat modeling framework.
In operational terms, organisations should look for four signals:
- Does the agent make context-based decisions at runtime rather than follow a fixed workflow?
- Can it request fresh secrets, tokens, or scoped access per task?
- Can it access multiple systems, databases, or tools without a human in the loop?
- Can every action be traced to a workload identity, policy decision, and audit record?
Where the answer is yes, separate governance is usually warranted. That often means workload identity as the primitive, short-lived credentials, intent-aware authorisation, and policy-as-code evaluated at request time. NHI lifecycle controls from Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs are useful here, but they need to be extended for agent autonomy, not just secret rotation. For threat context, NHIMG’s AI LLM hijack breach coverage shows how quickly tool-enabled systems can be abused once trust is misplaced.
These controls tend to break down in environments where agents are allowed to discover new tools dynamically and inherit broad network reach from a shared platform role.
Common Variations and Edge Cases
Tighter governance often increases operational overhead, so organisations need to balance control depth against deployment speed and developer friction. That tradeoff is especially visible in early-stage agent projects, where teams are tempted to start with a broad service account and refine later. Best practice is evolving, but the safer pattern is to grant the agent its own identity class from the start, then narrow scopes and approvals as usage stabilises.
There is no universal standard for this yet, especially for hybrid systems that combine deterministic automation with model-driven steps. A scripted workflow may still call LLM services, but if it cannot self-direct or expand its own access, ordinary IAM may remain appropriate. By contrast, a planner-executor agent that can decide next steps, retrieve data, or invoke downstream tools should usually sit under separate governance, consistent with the risk posture described in the Ultimate Guide to NHIs and NIST AI Risk Management Framework.
One important edge case is the “assistant” that starts as read-only but later gains write access, tool chaining, or delegated approval steps. Another is multi-agent orchestration, where each component seems harmless in isolation but the combined workflow creates privilege escalation paths. Organisations should reclassify the identity as soon as autonomy, reach, or decision authority changes. The strongest indicator is not the label on the service account, but whether the system can alter its own access path during execution.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Agent autonomy and tool use are central to deciding on separate governance. |
| CSA MAESTRO | GOV-2 | MAESTRO emphasizes governance for agent behaviour, not just authentication. |
| NIST AI RMF | GOVERN | AI RMF GOVERN covers accountability and risk ownership for AI systems. |
Classify any runtime decision-making agent under agentic controls, not ordinary app IAM.
Related resources from NHI Mgmt Group
- How can organisations decide whether an AI agent belongs in PAM, IAM, or NHI governance?
- How should organisations decide whether DLP belongs with IAM governance?
- How do identity teams decide whether an AI agent needs a separate governance model?
- How do IAM and PAM teams split responsibility for AI agent access?
