They should verify who owns the agent, what tools it can reach, how its access is revoked, and whether its behaviour is logged well enough for audit and incident response. If those answers are unclear, the organisation does not yet have governance, only access.
Why This Matters for Security Teams
Identity teams should treat AI agent access as a production change, not a routine entitlement update. Agents can act autonomously, chain tools, and reach systems that were never designed for unpredictable execution paths. That makes the pre-production review about more than permissions: it is about ownership, revocation, observability, and whether the organisation can prove what the agent did after the fact.
Industry guidance is still evolving, but the direction is clear in the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework: controls must reflect agent behaviour, not human job titles. NHI Management Group’s research on AI LLM hijack breach shows how quickly compromised identity paths can be abused once an attacker inherits machine access.
In practice, many security teams encounter agent misuse only after a tool call has already reached production data, rather than through intentional approval of the access path.
How It Works in Practice
The right evaluation starts with the agent’s operating model. Identity teams need to know whether the agent is a narrowly scoped workflow helper or a general-purpose autonomous system that can decide when to act, which tools to invoke, and how to respond to new context. Static RBAC is often too blunt for that environment because the agent’s access pattern is dynamic, not fixed. Current guidance suggests using workload identity, short-lived credentials, and policy decisions evaluated at request time.
That means confirming the following before production:
- Who owns the agent and who is accountable when it fails or is abused.
- What workload identity it uses, and whether that identity is cryptographically bound to the service or runtime.
- Which tools, APIs, and data stores it can reach, including indirect paths through plugins, MCP servers, or delegated workflows.
- How access expires, is revoked, and is rotated when the task ends or the model changes.
- Whether logs capture the agent’s prompts, tool calls, decisions, and returned outputs in a way that supports audit and incident response.
This is where zero standing privilege and just-in-time access become practical rather than theoretical. Ephemeral secrets, OIDC tokens, and workload identity systems such as SPIFFE are better aligned with agents than long-lived static keys. The security team should also test whether policy-as-code can evaluate intent, context, and destination before a tool call is executed. The NHIMG research on Ultimate Guide to NHIs and the OWASP NHI Top 10 both reinforce that identity governance for machines is strongest when access is measurable, revocable, and narrowly scoped.
These controls tend to break down when an agent can self-extend its workflow into new tools or inherited permissions because the original approval no longer matches the actual execution path.
Common Variations and Edge Cases
Tighter controls often increase deployment overhead, requiring organisations to balance safety against velocity. That tradeoff becomes sharper in environments where agents are embedded in CI/CD, customer support, or developer productivity workflows, because too much friction pushes teams to bypass review and reuse shared secrets.
There is no universal standard for this yet, so the practical question is how much autonomy the agent truly needs. A retrieval-only assistant may be managed with limited scoped access and strong logging, while a multi-step operational agent usually needs task-based approval, runtime policy enforcement, and automatic revocation after completion. Best practice is evolving toward context-aware authorisation rather than static role assignment, but human review still matters for high-impact actions such as data export, privilege changes, or production writes.
Another edge case is delegated access through orchestrators. If the agent does not directly hold the credential but can trigger a downstream service that does, the review must follow the chain of authority, not just the visible account. That is why vendor-provided “agent safety” claims are not enough on their own. Identity teams should validate the full blast radius, especially where the agent can reach secrets managers, source control, or cloud control planes. For broader threat modelling, the CSA MAESTRO agentic AI threat modeling framework and the MITRE ATLAS adversarial AI threat matrix are useful complements to identity review.
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 | A1 | Agent access review must account for autonomous tool use and misuse paths. |
| CSA MAESTRO | TRM | Threat modeling is needed to map agent autonomy, tool reach, and abuse cases. |
| NIST AI RMF | AI RMF frames governance, accountability, and monitoring for high-impact AI use. |
Model the agent's runtime trust boundaries and require controls for each external action path.
