Approval should go to the human or role that owns the underlying resource, not to the agent itself. If the request is for a tool, route it to the tool owner or delegated approver. If the request is for data, route it to the data steward or equivalent authority so the approval matches the actual privilege being granted.
Why This Matters for Security Teams
For agents, approval is not a clerical step. It is the control that determines whether an autonomous workload can reach a tool, dataset, or action boundary at all. If just-in-time access is approved by the wrong party, the result is often overbroad privilege, weak accountability, or a workflow that bypasses the actual risk owner. Current guidance suggests routing approval to the authority that can judge the privilege being granted, not to the system requesting it.
This is especially important because agent requests are not static like human login events. They can be generated dynamically, chained across tools, and repeated with slight variations. NHI Management Group research shows that 97% of NHIs carry excessive privileges, which makes approval quality directly relevant to blast radius. The risk is amplified in agentic environments covered by the OWASP Agentic AI Top 10 and the Ultimate Guide to NHIs, where access decisions must reflect what the agent is trying to do at runtime. In practice, many security teams discover approval gaps only after an agent has already been granted a privileged path through a tool or data plane.
How It Works in Practice
Approval should be mapped to the resource owner and the privilege scope, then enforced through workflow and policy controls. For a tool request, the approver is usually the tool owner or a delegated authority who understands the operational risk of that tool. For data access, the approver is the data steward, data owner, or a formally delegated custodian who can judge sensitivity, retention, and allowable use.
The practical pattern is to separate request intent from authorization decision. The agent submits a just-in-time request with machine-readable context such as task name, target resource, requested duration, expected actions, and justification. A policy engine then evaluates the request at runtime using least privilege, time limits, and environment signals. This aligns with emerging best practice in agentic security and with the runtime decision model described in the NIST AI Risk Management Framework and the CSA MAESTRO agentic AI threat modeling framework.
- Use the resource owner to approve the privilege, not the agent’s operator by default.
- Keep approvals short-lived and task-bound, with automatic expiry and revocation.
- Require workload identity and request context so the approver sees what the agent actually needs.
- Log who approved, what scope was granted, and what the agent did before revocation.
That operating model becomes stronger when paired with the OWASP NHI Top 10, because approval is only one part of the control chain. These controls tend to break down when organisations let generic on-call staff approve sensitive data access, because the approver lacks business context and the agent inherits privileges that were never meant for autonomous use.
Common Variations and Edge Cases
Tighter approval routing often increases operational overhead, requiring organisations to balance rapid automation against review quality. That tradeoff is real, especially when agents support incident response, software delivery, or customer operations where delays can affect service levels. Guidance is still evolving, so there is no universal standard for how much delegated approval authority is acceptable in each environment.
In low-risk cases, a delegated approver may be sufficient if the workflow is pre-approved, bounded by policy, and limited to a narrow set of resources. In higher-risk cases, especially for production secrets, regulated data, or destructive tools, approval should be explicit and traceable to the true owner. This is where the attack patterns described in the 52 NHI Breaches Analysis and the AI LLM hijack breach matter most: once an agent is granted the wrong access path, it can reuse that privilege faster than a human review process can react.
Best practice is evolving toward approvals that are both contextual and ephemeral. The closer the approval is to the actual asset owner and the shorter the privilege lifetime, the easier it is to justify the grant and revoke it cleanly. The model breaks down in highly federated environments where ownership is unclear, because no one can reliably validate the risk of the access being approved.
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 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A01 | Agentic systems need runtime approval controls, not static trust in the requester. |
| CSA MAESTRO | MAESTRO models agent workflows where approval must follow resource ownership and task risk. | |
| NIST AI RMF | GOVERN | AIRMF governance requires clear accountability for AI decisions and access outcomes. |
Assign approvers by asset class and require policy checks before agents receive any tool or data access.
