Treat the agent as a scoped identity with explicit permissions, audit trails, and revocation rules. The human owner should be verified strongly, but the agent still needs its own access boundaries. That prevents delegated activity from becoming invisible shadow access.
Why This Matters for Security Teams
When AI agents act for real users, the risk is not just access. It is delegation at machine speed, with enough autonomy to chain tools, reuse context, and expose data outside the user’s intent. Traditional user-centric IAM assumes a person is the actor; agentic systems break that assumption. Current guidance from the NIST AI Risk Management Framework and OWASP Agentic AI Top 10 points toward runtime control, not static entitlement alone.
This matters because agent sessions can look legitimate while still exceeding the user’s real intent. NHIMG’s AI Agents: The New Attack Surface report notes that 80% of organisations report agent actions beyond intended scope, including unauthorised system access and credential exposure. In practice, many security teams encounter this only after an audit gap, data leak, or destructive action has already occurred, rather than through intentional delegation design.
How It Works in Practice
The safest pattern is to treat the agent as a distinct scoped identity, not as a transparent extension of the human. The human user should still be strongly verified, but that proof should only authorize the delegation event. The agent then receives its own workload identity, short-lived token, and policy envelope that defines what it may do, for how long, and against which systems. That is the practical shift from static RBAC to context-aware authorization.
For implementation, teams typically combine four controls:
- Delegate once, then issue a separate agent identity bound to the task or workflow.
- Use just-in-time credentials with short TTLs, automatic revocation, and task-level expiry.
- Evaluate policy at request time with context, including the user, task, data sensitivity, and target tool.
- Log both the human request and the agent’s subsequent actions for non-repudiation and forensics.
This is where workload identity matters. Standards-based approaches such as SPIFFE/SPIRE or OIDC tokens give the platform cryptographic proof of what the agent is, while policy engines enforce what it may do right now. That is a better fit than long-lived API keys or shared service accounts, especially for autonomous workflows that can branch unexpectedly. NHIMG’s OWASP NHI Top 10 and CoPhish OAuth Token Theft via Copilot Studio both reinforce that delegated access must be narrow, observable, and easy to revoke. These controls tend to break down when agents are wired into legacy apps that only support broad API keys or coarse, user-level permissions because the platform cannot express task-scoped authority.
Common Variations and Edge Cases
Tighter delegation controls often increase operational overhead, requiring organisations to balance user convenience against blast-radius reduction. The tradeoff is especially visible when an agent needs to complete multi-step work across several systems, because each step may need separate authorization and fresh credentials.
There is no universal standard for this yet, but current guidance suggests a few edge-case rules. If the agent is customer-facing, treat every action as potentially user-visible and fraud-sensitive. If the agent can call external tools or browser automation, assume prompt injection or tool abuse may redirect it mid-task. If the workflow is highly privileged, put human approval gates around sensitive steps rather than the full session.
Another common mistake is assuming a verified human owner eliminates the need for agent boundaries. It does not. Ownership proves delegation intent; it does not constrain the agent’s downstream behavior. For that reason, best practice is evolving toward separate audit trails, per-task revocation, and explicit denial rules for sensitive actions like credential export, record deletion, and privilege escalation. NHIMG’s Ultimate Guide to NHIs — 2025 Outlook and Predictions is useful here because it frames identity as an operational control surface, not just an authentication problem.
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, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | Agentic apps need runtime controls for delegated actions and tool use. | |
| CSA MAESTRO | MAESTRO covers threat modeling and governance for autonomous agent workflows. | |
| NIST AI RMF | AI RMF supports accountable, measurable controls for agent behaviour. | |
| OWASP Non-Human Identity Top 10 | NHI-03 | Short-lived agent credentials reduce exposure from delegated access. |
| NIST Zero Trust (SP 800-207) | SP 800-207 | Zero Trust fits agents because trust must be re-evaluated on every request. |
Model delegation paths, escalation points, and revocation steps before production rollout.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on July 11, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
