Yes. Organisations should establish ownership, least privilege, monitoring, and revocation for machine identities before broadening agentic AI use. Without those controls, each new agent can multiply blast radius and create shadow access that is hard to unwind after an incident.
Why This Matters for Security Teams
For agentic AI, the question is not whether identity governance is useful, but whether the organisation can contain autonomous access before agents begin chaining tools, calling APIs, and reusing secrets at machine speed. Static role models assume predictable human patterns; agents are goal-driven, context-sensitive, and often operate with wider tool reach than their owners expect. That is why guidance from the OWASP Top 10 for Agentic Applications 2026 and the NIST AI Risk Management Framework increasingly points toward runtime control, accountability, and bounded execution rather than broad pre-assigned access.
Identity governance is the control layer that makes agent expansion survivable. Without it, a single compromised token can become repeated access across orchestration layers, plugins, and downstream systems. NHIMG research shows how fast abuse can follow exposure: in the LLMjacking research, attackers attempted access to exposed AWS credentials within an average of 17 minutes. In practice, many security teams discover that their “AI rollout” has also created a hidden identity programme after the first incident, not during design.
How It Works in Practice
For autonomous systems, the governance sequence should start before broad deployment: inventory every machine identity, classify which agents can act independently, and bind each agent to a named owner, a bounded purpose, and a revocation path. That is consistent with the control focus in the Ultimate Guide to NHIs and the agentic risk themes in OWASP Agentic Applications Top 10. The practical objective is to replace standing access with JIT credential provisioning, short TTL secrets, and explicit approval paths for sensitive actions.
- Issue workload identity first, so the agent proves what it is before it gets anything useful.
- Use intent-based authorisation so access is granted for a specific task, not a generic role.
- Prefer ephemeral secrets and token exchange over long-lived API keys and shared service accounts.
- Log every tool call, secret use, and privilege change, then make revocation automatic when the task ends.
- Test for lateral movement and tool chaining, not just prompt injection.
That implementation model aligns well with CSA MAESTRO agentic AI threat modeling framework and the NIST Cybersecurity Framework 2.0, because both encourage control mapping, monitoring, and recovery rather than one-time approvals. These controls tend to break down when agents inherit legacy shared credentials across CI/CD, chatops, and SaaS plugins because revocation becomes ambiguous and attribution is lost.
Common Variations and Edge Cases
Tighter identity governance often increases operational overhead, requiring organisations to balance faster agent experimentation against stronger change control and secrets hygiene. That tradeoff is real, especially where teams want always-on agents for support, code assistance, or workflow automation. Best practice is evolving, but there is no universal standard for how much autonomy a low-risk agent should receive without human re-approval.
Some environments need more nuance. A read-only retrieval agent may tolerate narrower controls than a code-changing deployment agent. In regulated workflows, it may be necessary to pair RBAC with policy-as-code and runtime checks, because static RBAC alone cannot express context such as ticket status, data sensitivity, or time window. For deeper NHI lifecycle detail, the Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs is useful, while the Top 10 NHI Issues page helps teams prioritise remediation. In highly distributed multi-agent systems, the model becomes harder because one agent may delegate to another, and standing access can silently reappear through the back door.
Where this guidance is weakest is in fast-moving proof-of-concept environments that skip ownership, expiry, and revocation design in favour of speed. That is usually where shadow access grows first.
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 | Agentic systems need runtime controls because static roles fail for autonomous actions. |
| CSA MAESTRO | MAESTRO models agent threats across autonomy, tools, and identity lifecycle. | |
| NIST AI RMF | GOVERN | AI RMF governance is needed to assign accountability for agent behaviour and access. |
Define task-scoped policy checks and block sensitive tool use without explicit runtime approval.
Related resources from NHI Mgmt Group
- What is the Agentic AI identity governance framework organisations should adopt?
- What makes agentic AI an NHI governance issue?
- Should organisations prioritise external exposure or internal credential governance first?
- What are the emerging security controls needed for Agentic AI identity governance?
