How should organisations govern access for AI agents and other non-human identities?

What governance model fits AI agents and other non-human identities?

Governance works best when AI agents are treated as first-class identities, not as anonymous automation. That means a named owner, an inventory record, a business purpose, a risk tier, and a clear approval path for any access they receive. Without that structure, organisations end up with machine access that is easy to create, hard to explain, and even harder to remove.

The practical test is whether each non-human identity can be described the same way you would describe a privileged user: what it is for, who owns it, what it can touch, and when it must be revoked. If those answers are missing, the organisation does not really govern the identity, it merely tolerates it.

How should access be granted and constrained in practice?

Access should be issued on a per-purpose basis, with the smallest privilege set that still lets the agent complete the task. Short-lived credentials, just-in-time elevation, and workload-specific identities are safer than shared secrets or standing access because they reduce the window in which an exposed credential can be reused. Authentication should be strong enough to prove the workload or agent really is the intended actor, not just any script that knows a token.

That discipline matters most where an agent can act across tools, environments, or systems of record. For those cases, the access model should be explicit about scope, approval, and delegation. A useful reference point is the AI Agent Authorisation Guide, which centres least privilege, task-scoped access, and per-action policy decisions.

Organisations should also separate identity from convenience. Reusing one credential across many agents, environments, or services collapses accountability and increases blast radius. A better design is to bind each identity to one purpose, one owner, and one set of controls, then require re-approval when the purpose changes.

How do monitoring, review, and revocation keep non-human access under control?

Governance is incomplete unless access is continuously observable and revocable. Every non-human identity should generate logs that show who or what used it, what it accessed, and which policy decision allowed the action. That evidence is what turns a machine identity from an opaque trust assumption into something security teams can review, investigate, and retire.

Lifecycle control is the other half of the problem. A good governance model includes automatic expiry, periodic recertification, and fast revocation when an agent is no longer needed, changes purpose, or behaves outside its expected pattern. The AI Agent Observability, Audit and Incident Response Guide is useful here because it ties logging, attribution, and revocation to the operational reality of agent behaviour.

Discovery also matters. Unowned service accounts, forgotten API keys, and shadow AI integrations are often the first sign that governance has drifted from policy into exception handling. The Shadow AI and AI Agent Discovery Guide helps frame the inventory problem as an ongoing control, not a one-time clean-up exercise.

Risk and Threat Considerations

Non-human identities fail governance in predictable ways: overprivileged access, long-lived credentials, shared secrets, and missing ownership. Those weaknesses create a direct path from routine automation to material compromise, because an attacker or careless integration can reuse the same trusted identity at machine speed.

Failure mechanism: If an AI agent or other NHI has standing access, broad scopes, or credentials that are not tightly bound to purpose and lifecycle, compromise of that identity can become immediate reuse, lateral movement, or data exposure across connected systems.

Impact: The organisation loses both containment and attribution. A single abused machine identity can trigger destructive actions, exfiltration, or policy-bypassing transactions before human teams notice, and the absence of clear ownership makes investigation and recovery slower.

Practitioner Guidance

What to prioritise: Start with ownership, inventory, and privilege review before adding more automation. If you cannot say who owns the identity and why it exists, do not expand its access.

What to verify: Confirm that each agent or NHI has a unique identity, a defined purpose, expiry or recertification logic, and logs that let you attribute its actions without relying on shared credentials or human credentials in disguise.

Decision rule: If the identity can reach production data, production tools, or release paths, require least privilege, short-lived access, and an explicit revocation path before deployment. If it cannot be scoped that tightly, treat the design as unfinished.

Practitioner takeaway: The central governance decision is not whether AI agents may have access, but whether every granted action remains bounded, attributable, and removable throughout the identity’s full lifecycle.

Related resources from NHI Mgmt Group

• Guide: Ultimate Guide to NHIs
• How should organisations govern SCIM for AI agents and other non-human identities?
• Should organisations govern AI agents like other non-human identities?
• How should security teams govern non-human identities that have persistent access?
• How should security teams govern access when bots and AI agents act like non-human identities?