Should AI agents be reviewed separately from human and service accounts?

Why Unified Review Matters for AI Agents

AI agents should not be reviewed in a separate identity lane because their access often looks like a blend of human approval, service-account execution, and delegated tool use. The control question is not whether the actor is human, automated, or hybrid; it is whether the identity has comparable privilege, ownership, and lifecycle governance. Separate review paths make it easier to miss when an agent has broader tool reach, longer-lived credentials, or weaker accountability than the accounts around it.

That blind spot matters because agentic systems can act quickly, chain tools, and operate outside the normal pace of human oversight. Current guidance from the OWASP Top 10 for Agentic Applications 2026 and the NIST AI Risk Management Framework supports treating these systems as governed assets whose impact must be evaluated, not as novelty accounts that sit outside normal review discipline.

In practice, many security teams discover over-privileged agents only after a workflow failure, a tool misuse, or a messy offboarding event has already exposed the gap.

How Unified Identity Review Works in Practice

Unified review means the same recertification workflow covers the agent, the human sponsor, and any service account or workload identity involved in the chain. The reviewer should be able to see who owns the agent, what it can invoke, what credentials it uses, which environments it touches, and whether its permissions exceed the minimum needed for its current task set. That is the practical difference between reviewing an “AI feature” and reviewing an identity with real operational reach.

This also changes how teams think about lifecycle state. An agent may be technically active, but if its sponsoring team changed, its toolset expanded, or its underlying secret was not rotated, the review is already stale. A useful pattern is to compare the agent against the nearest human and service account equivalents so the reviewer can spot privilege inflation, shadow ownership, or mismatched termination dates. The same approach aligns well with the Ultimate Guide to NHIs — 2025 Outlook and Predictions, which frames machine identity governance as an inventory and lifecycle problem, not just an access-control problem.

• Review the agent, sponsor, and backend identity together.
• Check ownership, purpose, access scope, secret age, and revocation path in one record.
• Compare the agent’s effective reach to the human role it supports and the service account it depends on.
• Require an explicit exception if the agent has broader access than the user or workflow it automates.

Where this breaks down is in environments that treat agents as ephemeral experiments with no stable owner, because review cannot be trusted when the identity’s purpose and sponsorship are changing faster than the control cycle.

Common Variations and Edge Cases

Tighter review often increases administrative overhead, so organisations have to balance speed against the risk of invisible privilege drift. That tradeoff is real in teams with many short-lived agents, but current guidance suggests the answer is not a separate review silo. It is a shared identity model with stronger metadata, clearer ownership, and review criteria that can handle both human and non-human actors.

Edge cases matter most when the agent is embedded in a platform account, when multiple teams can invoke the same agent, or when the agent’s permissions are assembled dynamically from connectors and tokens. In those environments, the identity on paper may look harmless while the effective permissions are much broader. A separate review path often fragments that picture. Better practice is to preserve one identity context and then add tags or attributes for autonomy level, delegated scope, and approval boundaries. The CSA MAESTRO agentic AI threat modeling framework is helpful here because it reinforces that agent behaviour, tool access, and orchestration context need to be evaluated together rather than in isolation.

Practitioner takeaway: if an agent can create, modify, or act through production systems, it belongs in the same recertification logic as the other identities that enable that reach.

Risk and Threat Considerations

The material risk is identity blind spot: when AI agents are reviewed separately, organisations can miss excessive privilege, stale ownership, or unreconciled credentials that materially expand blast radius. The threat is not that the agent is “different,” but that its delegated reach can be abused, inherited, or left unrevoked in ways that are harder to notice than with ordinary accounts.

Failure mechanism: Separate review streams fragment the evidence needed to compare effective access across human, service, and agent identities. That weakens least-privilege enforcement, slows revocation, and makes it easier for compromised tokens, over-broad connectors, or abandoned agent accounts to persist unnoticed.

Impact: The result can be unauthorised system actions, exposed data, untracked tool invocation, and a weak audit trail that prevents teams from proving who had authority when the agent acted.

Practitioner Guidance

What to prioritise: Put agents into the same certification and offboarding workflow as the human owner and any backend service identity. The review should answer one question first: does the agent have more reach than the role or process it is meant to represent?

What to verify: Confirm the identity record contains an accountable sponsor, a revocation path, and a current list of tools, secrets, and environments. If any of those are missing, treat the agent as incompletely governed, not merely incompletely documented.

Decision rule: If a reviewer cannot compare the agent’s privilege to a nearby human or service account in the same view, the control is too fragmented to trust. Escalate that as a governance defect rather than accepting it as a tooling limitation.

Practitioner takeaway: The review objective is comparability, not categorisation; once an agent can influence production outcomes, it must be governable against the same evidence set used for other identities.

Related resources from NHI Mgmt Group

• How can organisations govern AI agents that use service accounts and tokens?
• Why do AI agents make non-human identity governance harder?
• Why do AI agents create new risk in non-human identity management?
• Why do AI agents increase non-human identity risk in existing IAM programmes?