They should treat them as unmanaged identities first, then bring them into an inventory, ownership, and monitoring process before expecting policy compliance. If an agent is outside identity controls, it cannot be certified with the same confidence as a governed workload or user account.
Why Unmanaged AI Agents Must Be Governed as Identities First
An AI agent that is not connected to IdP or PAM is not just “missing a control”, it is operating outside the normal assurance chain. The practical issue is not whether the agent can still work, but whether the organisation can prove who owns it, what it can do, and when to remove or constrain it.
That is why AI Agent Authorisation Guide matters: if an agent can act, it needs explicit decisioning around scope, delegation and approval, rather than informal trust in a deployed tool.
In governance terms, the right starting point is to treat the agent as an unmanaged identity until it is inventoried, assigned ownership and placed under monitoring. Only then can policy be applied consistently, because policy that cannot be tied to a governed principal is usually just documentation.
What Changes When the Agent Is Outside IdP or PAM
An unmanaged agent has a different risk profile from a normal user or service account because there is no reliable anchor for lifecycle control. That affects onboarding, offboarding, review, exception handling and incident response, especially when the agent can access production data, tools or workflows.
Agentic AI Identity Guide is the natural reference point here: ownership, registration, authentication and retirement are the controls that turn an agent from an ad hoc capability into a governed actor.
Zero Trust for AI Agents reinforces the operating principle that standing trust should not be assumed just because the agent is internal. Verify the principal and the request, then decide access per action rather than granting broad standing authority.
When an agent sits outside PAM, you also lose the usual privilege review and revocation discipline. That means even a well-intentioned deployment can accumulate hidden access paths, orphaned tokens, or broad entitlements that outlive the business need.
How to Bring Unmanaged Agents Under Control Without Guesswork
The most useful governance model is staged: discover the agent, assign an accountable owner, record what systems it can reach, then decide what must be mediated through policy. That sequence avoids the common mistake of trying to “certify” an entity before it is even known to the organisation.
Shadow AI and AI Agent Discovery Guide supports the first step, which is finding agents through the access trails they leave behind, such as OAuth grants, API keys and connected services. Discovery is not governance by itself, but it is the prerequisite for it.
AI Agent Observability, Audit and Incident Response Guide supports the monitoring layer. Once the agent is in inventory, the organisation needs action logs, attribution and a tested kill switch so abnormal behaviour can be contained quickly.
Agentic AI Security Guide is useful where the governance question turns into a control-design question: identity is only one part of the attack surface, but it is the part that determines whether guardrails can be enforced at all.
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 addresses the attack surface, NIST SP 800-53 Rev 5 sets the technical controls, and ISO/IEC 27001:2022 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Unmanaged agents create privilege and delegation risk. |
| Recommendation — Restrict agent authority to the minimum per action. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Service Identification and Authentication | Disconnected agents need governed machine authentication. |
| AC-6 — Least Privilege | Governance hinges on limiting an agent's effective access. | |
| AU-6 — Audit Record Review, Analysis, and Reporting | Unmanaged agents need monitoring before policy can be trusted. | |
| Recommendation — Authenticate agent-to-service interactions with managed credentials. Limit each agent to the minimum permissions needed. Review agent activity logs and alert on anomalous actions. | ||
| ISO/IEC 27001:2022 | A.5.16 — Identity management | Agent governance requires identities to be registered and owned. |
| Recommendation — Maintain an inventory of agent identities with accountable owners. | ||
Practitioner Guidance
What to prioritise: Put every disconnected agent into an inventory before debating fine-grained policy. If you cannot name the owner, the runtime, the connected systems and the revocation path, you do not yet have a governable asset.
Decision rule: If the agent can reach production systems or sensitive data, require explicit ownership, monitoring and revocation before permitting normal operation. If it cannot be placed under those controls, treat its access as temporary exception risk, not as an approved baseline.
What to verify: Confirm that the agent has a clear human owner, a recorded purpose, an access boundary and a tested removal process. A review that only checks whether the agent “still works” is not a governance review.
Common mistake: Teams often assume that an AI agent is safe because it is bounded by the application it lives in. In practice, unmanaged agents are often bounded only by whatever credentials or API paths they inherit, which is a much weaker boundary.
Practitioner takeaway: Governance starts with recognition, not approval, so the first control objective is to make the agent attributable and revocable before expecting it to comply with policy.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on October 10, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org