Why do agent user and on-behalf-of flows complicate IAM reviews?

Why This Matters for Security Teams

agent user and on-behalf-of flows blur the line between human intent and machine execution, which makes ordinary IAM review methods unreliable. A token may look like a normal user session while actually representing delegated automation, inherited workload privilege, or a chained tool action. That is exactly where certification teams miss risk: the review is focused on the account name, not the acting context.

For non-human identity programs, that distinction matters because the control objective is not just “who signed in” but “what identity executed, under what authority, and for which purpose.” NHI Management Group research shows that only 19.6% of security professionals have strong confidence in their organisation’s ability to securely manage non-human workload identities, which aligns with the review gaps seen in mixed human-plus-agent environments. Guidance from Ultimate Guide to NHIs — 2025 Outlook and Predictions and the NIST AI Risk Management Framework both point toward context-aware governance rather than static account inspection.

In practice, many security teams discover delegated privilege drift only after a helpdesk escalation, incident review, or token misuse has already happened, rather than through intentional certification.

How It Works in Practice

The practical problem starts when a human identity triggers an agent, which then receives a token to act on that person’s behalf. In a clean design, the review trail should separate at least three actors: the human requester, the agent or workload identity, and the downstream service account or OAuth client that actually executed the action. If those layers collapse into one line item, reviewers cannot tell whether access was direct, delegated, or inherited.

Current guidance suggests moving IAM reviews toward actor-aware logic. That means evaluating the originating user, the delegated scope, the workload identity, token TTL, and the runtime context together. For agentic systems, the runtime layer is especially important because an autonomous agent can chain tools, request new privileges, or expand its activity in ways a static entitlement report will not show. This is why the patterns discussed in OWASP NHI Top 10 and the OWASP Agentic AI Top 10 matter for IAM governance, not just application security.

• Tag delegated sessions so review tools can distinguish human login from machine execution.
• Bind tokens to workload identity and purpose, not only to a user account.
• Use short-lived credentials and revoke them when the task ends.
• Record the full chain of custody: user, agent, tool, scope, and downstream resource.
• Route certification decisions through policy rules that understand delegation and on-behalf-of semantics.

For implementation, standards work on workload identity and policy evaluation is more useful than trying to force these sessions into traditional sign-in review. These controls tend to break down in federated SaaS stacks with opaque delegated scopes because the downstream audit trail often omits the agent context that reviewers need.

Common Variations and Edge Cases

Tighter delegated-access review often increases operational overhead, requiring organisations to balance stronger assurance against slower certification cycles and more complex tooling. That tradeoff becomes most visible in environments with many SaaS integrations, external contractors, or multi-hop automation where one user action triggers several backend identities.

There is no universal standard for this yet, but best practice is evolving in one clear direction: treat on-behalf-of flows as a distinct identity class, not as a normal user login. That means separate approval logic, separate revocation logic, and separate evidence fields for auditors. It also means that emergency access, service desk automation, and AI copilots should not share the same review workflow as direct human sessions. The delegated path may be legitimate, but legitimacy does not remove the need for traceability.

Teams often get tripped up when a single user owns both a human account and an agent that performs privileged work. In those cases, a review that only checks the user’s role will overstate confidence and understate actual authority. For governance models that account for this, CSA MAESTRO agentic AI threat modeling framework and the NHI Management Group guidance on workload visibility provide a better operational lens than conventional access certification alone.

In practice, these flows are hardest to review when the platform hides delegation metadata, because auditors then have no reliable way to tell whether a “user action” was truly human or was executed by an agent carrying inherited privilege.

Related resources from NHI Mgmt Group

• What is the difference between human identity governance and AI agent governance?
• Why do AI agents increase non-human identity risk in existing IAM programmes?
• When does AI agent access create more risk than it reduces?
• What is the difference between governing human access and governing AI agent access?