What should identity teams review when an AI agent is allowed to delegate?

Review the full delegation chain, not only the first identity that was authorised. Each hop should inherit a smaller permission set, a clear purpose, and a traceable authority link. If that attenuation is missing, delegated access can expand faster than the original approval ever intended.

Why This Matters for Security Teams

Delegation is where AI agent risk changes shape. A single approved action can become a chain of tool calls, sub-agents, and downstream permissions if the identity team only reviews the first hop. That matters because agentic systems are goal-driven and can adapt their path in ways a static approval model never anticipated. Current guidance from OWASP Agentic AI Top 10 and NIST AI Risk Management Framework both point toward runtime controls, not one-time trust decisions.

Identity teams should review whether the delegation path preserves least privilege at every hop, whether each transfer has a documented business purpose, and whether the original authorisation still applies after the agent has combined tools or delegated further. This is especially important for Ultimate Guide to NHIs governance because NHI sprawl already creates blind spots; adding autonomous delegation makes those gaps operationally significant. In practice, many security teams encounter delegated overreach only after an agent has already moved data or touched systems outside the intended scope, rather than through intentional design review.

How It Works in Practice

The review should start with the delegation contract, not the agent persona. Identity teams need to validate what is being delegated, to whom, for how long, and under which policy. A sound design uses workload identity as the proof of what the agent is, then issues JIT credentials or short-lived tokens only for the task at hand. Static RBAC alone is usually too blunt for autonomous systems, because an agent may pursue different paths based on context. Better practice is emerging around intent-based authorisation and real-time policy evaluation, where the system checks the current request, current data sensitivity, and current trust posture before allowing the next hop.

That means reviewing:

• Whether each delegated token is narrower than the parent token and expires sooner.
• Whether the agent can only delegate to approved workload identities, not generic service accounts.
• Whether downstream tools enforce policy-as-code at request time, rather than trusting the original grant indefinitely.
• Whether secrets are ephemeral and bound to the specific task, rather than copied into prompts, logs, or shared caches.
• Whether every hop is logged with an authority link so investigators can reconstruct who authorised what, and when.

This aligns with the direction of the CSA MAESTRO agentic AI threat modeling framework and the OWASP NHI Top 10, both of which emphasise tool misuse, overprivilege, and chaining risk. For operational validation, teams should also compare the delegation path against the visibility lessons in the AI Agents: The New Attack Surface report. These controls tend to break down when agents are allowed to delegate into legacy systems that only understand long-lived API keys, because the old systems cannot enforce task-scoped expiry or runtime context.

Common Variations and Edge Cases

Tighter delegation controls often increase integration overhead, requiring organisations to balance speed of execution against auditability and blast-radius reduction. That tradeoff is real, especially in environments where agents coordinate across multiple business units or SaaS tools. There is no universal standard for this yet, so best practice is evolving toward a layered model: approve the class of task, constrain the maximum authority, and re-evaluate every delegated step in real time.

Two edge cases deserve attention. First, nested agents can create hidden privilege amplification if a parent agent passes an authority token to a child agent that then requests broader access. Second, human-in-the-loop approval can become symbolic if the reviewer sees only the initial request and not the downstream delegation tree. Identity teams should therefore require traceability across the full chain, not just the first grant. The most useful control is often not a single deny rule, but a policy that forces re-authentication, re-authorisation, or fresh JIT issuance at each boundary. That approach fits the guidance in Ultimate Guide to NHIs and the governance emphasis in NIST AI Risk Management Framework.

In highly regulated or high-volume automation environments, delegation reviews should also ask whether a task can be restructured to avoid delegation altogether. When the agent must delegate, constrain it to a known workload identity, use short-lived credentials, and revoke standing access immediately after task completion.

Related resources from NHI Mgmt Group

• How should security teams assess AI agent behaviour beyond identity checks?
• What is the difference between human identity governance and AI agent governance?
• How should security teams govern machine identity credentials in agentic AI environments?
• Why is identity such a critical factor in securing AI agent systems?