Treat approval as a runtime control, not a post-denial exception. The policy should be able to hold, retry or resume actions based on a qualifying approval event before the agent completes the next step. That keeps human oversight inside the workflow rather than outside it.
Why This Matters for Security Teams
When human approval is part of agent execution, the control point moves from a one-time access grant to a runtime decision. That matters because autonomous or semi-autonomous agents do not behave like humans with stable workflows, and static RBAC or ticket-based exceptions can leave a dangerous gap between “approved in principle” and “safe to continue now.” Current guidance increasingly aligns with OWASP Agentic AI Top 10 and NIST AI Risk Management Framework thinking: approval must be bound to the specific action, context, and time window, not just the actor.
NHI Management Group research shows the risk is not theoretical. The 2024 Non-Human Identity Security Report found that 88.5% of organisations say their non-human IAM practices lag behind or merely match human IAM, which helps explain why approval workflows often get bolted on as manual gates instead of policy-enforced execution controls. In practice, many security teams discover this mismatch only after an agent has already queued the next action and human review arrived too late.
How It Works in Practice
Human approval should be treated as a first-class policy input inside the agent workflow. The IAM system should be able to pause execution, retain state, and resume only when a qualifying approval event is received for the exact task, scope, and risk level. That means the approval is not a generic “yes” for the agent; it is a runtime authorization signal bound to a specific action. This approach fits emerging agentic guidance in CSA MAESTRO agentic AI threat modeling framework and the operational model described in Ultimate Guide to NHIs, where short-lived access, revocation, and visibility are core controls.
In practice, teams usually implement this with three linked controls:
- Intent-based policy checks that evaluate what the agent is trying to do at request time, not only who the agent is.
- Just-in-time ephemeral credentials that are issued only after approval and expire quickly after the approved step completes.
- Workflow state management that can hold, retry, or resume without forcing the agent to restart from scratch.
Workload identity is the right primitive for this pattern. The approval event should unlock a workload-scoped token or assertion, not a long-lived secret, so the system can prove what the agent is and what it is allowed to do in that moment. That is why current implementation guidance often points to policy-as-code, OPA-style evaluation, and short-lived tokens rather than static allowlists. This becomes especially important when agents chain tools, call external APIs, or trigger downstream automations that a human approver never directly sees. These controls tend to break down when approval is handled in a separate ticketing system because the agent can continue to evolve its context while the human decision is still outside the execution path.
Common Variations and Edge Cases
Tighter approval controls often increase latency and operational overhead, so organisations need to balance safety against workflow friction. The tradeoff is real: if every low-risk action requires a human click, teams create approval fatigue; if approvals are too broad, they become meaningless. Best practice is evolving, but there is no universal standard for this yet.
Some environments require stronger separation between approval and execution. For example, in high-risk production systems, the approver may need to validate both the intent and the destination resource, while in development or sandbox environments a lighter approval may be acceptable. In multi-agent pipelines, a single approval event should not automatically grant downstream agents the same privilege unless the policy explicitly allows delegation. This distinction matters because approval for one agent step does not equal trust in all subsequent steps.
Security teams should also avoid treating approval as a one-time exception to broad standing access. That pattern reintroduces the very risk JIT is meant to remove. The stronger model is per-task, per-step, time-bounded authorization with revocation on completion or timeout. NHIMG analysis of incidents such as CoPhish OAuth Token Theft via Copilot Studio and the Analysis of Claude Code Security shows why approval must stay close to execution when agents can pivot across tools and identities. In mixed human-agent workflows, the control fails most often when approval is granted for a broad session rather than a single bounded action, because the agent can reuse that trust in a different context before anyone notices.
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 CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Covers agentic authorization and unsafe action execution after approval. |
| CSA MAESTRO | T1 | Addresses threat modeling for agent workflows that mix human approval and autonomy. |
| NIST AI RMF | Supports governance of context-aware decisions for autonomous systems. | |
| OWASP Non-Human Identity Top 10 | NHI-03 | Short-lived credentials reduce risk when approval gates execution. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access control applies to approved agent actions. |
Issue ephemeral credentials only after approval and revoke them immediately after the task.
