Yes. AI agents should be governed as non-human identities because they authenticate, request data, invoke tools, and act without direct human supervision. Treat them with the same controls used for service accounts, API keys, and certificates: unique identity, least privilege, strong authentication, secret rotation, scoped access, logging, continuous review, and revocation. Add agent-specific policy for autonomy, tool use, and approved data paths to reduce misuse and unintended actions.
How AI agent governance fits the non-human identity model
AI agents belong in the same governance conversation as service accounts and API keys because they are delegated actors with their own authentication, access paths, and operational side effects. The important distinction is not whether the actor is “human” or “AI”, but whether it can obtain identity, request resources, and take actions that create security impact. That makes the control problem familiar, even when the runtime is more dynamic.
What changes with agents is that the access path is often more fluid. An agent may obtain short-lived tokens, call tools on behalf of a user, or move between contexts during a task, which means identity and authorisation decisions need to be explicit at the point of use rather than assumed from the surrounding application. That is why agent governance is best treated as a live identity and access problem, not just an application feature.
When organisations manage the agent as a first-class identity, they can answer basic control questions more cleanly: who or what issued the credential, what the agent may do, which tools it may call, which data it may touch, and when the access must stop. That framing is also easier to align with Ultimate Guide to NHIs, because the same lifecycle logic applies whether the non-human actor is a service account, workload, or AI agent.
What controls matter most for AI agents
The core controls are the same ones used for other machine identities, but they need to be applied with more precision. Unique identity matters because shared credentials make attribution and revocation unreliable. Least privilege matters because agents can chain tools quickly and overreach before a human notices. Strong authentication matters because an agent that can be impersonated becomes an easy pivot point for misuse.
Secret hygiene is equally important. If an agent can hold long-lived API keys, reusable tokens, or broad OAuth grants, it can outlive the task it was created for and continue operating after the original business need has passed. Rotation and revocation should therefore be routine, not exceptional, and the access scope should be tied to the specific workflow, environment, and data path the agent actually needs.
Governance also needs an explicit decision layer for autonomy. Not every agent action should be pre-approved, but not every action should be free-running either. A practical policy separates low-risk actions that can proceed under a bounded policy from higher-impact actions that require human approval, tighter tool restrictions, or narrower data access. That is why many teams use AI Agent Authorisation Guide and Zero Trust for AI Agents as practical references for access scoping and continuous verification.
Why this becomes a security issue at scale
AI agents expand the blast radius of mistakes because they can operate quickly, across multiple systems, and with delegated trust. A single over-scoped agent can read data it should not see, trigger changes outside its intent, or propagate bad decisions into downstream tools. The risk is not just theft of the agent’s secret, but misuse of the authority attached to the identity.
That is especially true when agents are connected to ticketing, code repositories, cloud consoles, or customer data systems. In those settings, the wrong tool grant can become an operational incident even without a classic intrusion. Attackers also like these paths because they turn legitimate delegation into a ready-made abuse channel. An agent that can approve, retrieve, transform, or forward data can become a convenient mechanism for exfiltration, privilege abuse, or destructive action if the guardrails are weak.
This is the same basic control failure seen in broader agent security work: identity is not the only target, but it is often the easiest place to prevent collateral damage. For a deeper threat-centric view, Agentic AI Security Guide and Top 10 Agentic AI Identity Issues help connect overprivilege, trust abuse, and tool misuse to concrete control failures.
Risk and Threat Considerations
AI agents increase exposure when they inherit human trust, broad API permissions, or reusable credentials without tight lifecycle controls. The failure mode is usually not “the model was clever”, but “the delegated identity could do too much for too long” and no one noticed until after the action chain completed.
Failure mechanism: An attacker, malicious prompt, or unsafe integration can exploit the agent’s token, delegated grant, or tool access to perform actions that appear legitimate to downstream systems. If the agent is not uniquely attributable and continuously constrained, those actions can be difficult to distinguish from authorised use.
Impact: Organisations can get silent data exposure, unauthorised transactions, destructive changes, or lateral movement through trusted workflows. Recovery is harder when the agent shares credentials, operates across environments, or leaves weak audit evidence.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | AI agents can hold excessive delegated access and tool scope. |
| NHI-07 — Long-Lived Secrets | Agent credentials and tokens must be short-lived and rotated. | |
| NHI-10 — Human Use of NHI | Agents may act on behalf of people and inherit human trust boundaries. | |
| Recommendation — Apply least privilege and narrow every agent grant to the minimum task scope. Rotate agent secrets regularly and replace reusable credentials with shorter-lived alternatives. Separate human and agent authority so delegated actions stay explicit and attributable. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent identity and privilege are central to misuse and overreach risks. |
| ASI02 — Tool Misuse | Agent tool access is a core governance concern in this question. | |
| Recommendation — Constrain agent privileges and require policy checks before high-impact actions. Restrict which tools an agent may call and review those permissions continuously. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Service Identification and Authentication | AI agents behave like services authenticating to other services. |
| AC-6 — Least Privilege | The answer centres on scoped access and minimal authority for agents. | |
| AU-2 — Event Logging | Agent actions need durable logging for attribution and review. | |
| Recommendation — Authenticate agents as service-like actors and bind credentials to their specific function. Limit each agent to only the permissions needed for its approved task. Log agent actions with enough context to reconstruct who or what did each step. | ||
| NIST Zero Trust (SP 800-207) | PA-1 — Policy Engine | Agent access should be evaluated at request time, not assumed. |
| Recommendation — Enforce per-request policy decisions before an agent can act or fetch data. | ||
Practitioner Guidance
What to prioritise: Treat every agent that can read, write, or invoke tools as a governed identity with an owner, a lifecycle, and an explicit revocation path. If you cannot point to the identity record, credential source, tool scope, and approval boundary, the agent is not governed enough.
What to verify: Confirm that the agent’s permissions are narrower than the user or service account it may represent, that credentials are not shared across environments, and that logs can attribute each meaningful action to a specific agent instance. Also verify that the policy for autonomous actions is written down, not implied by the workflow.
Decision rule: If the agent can affect production data, security settings, or external communications, require tighter approval, stronger monitoring, and shorter-lived access than you would for a normal application integration. If it only summarises or drafts content, the control burden is lower, but it still needs traceable identity and bounded data access.
Practitioner takeaway: The governance test is simple: if the agent can act, it needs a real identity, constrained authority, and a way to be shut off cleanly before trust turns into unchecked automation.
Related resources from NHI Mgmt Group
- How should organisations govern SCIM for AI agents and other non-human identities?
- How should security teams govern access when bots and AI agents act like non-human identities?
- Should organisations govern API keys and service tokens like other non-human identities?
- Should organisations treat AI pentesting agents like non-human identities?