Topic starter
30/10/2026 2:20 pm
TL;DR: Agentic AI governance breaks down when agents can act with valid credentials inside controls that all appear to work, according to Anjuna, because the real decision point is whether the action should happen now, in this context. Identity alone is not enough; the enforcement layer must sit in the execution path and match autonomy to consequence.
Editorial analysis by NHI Mgmt Group, based on content published by Anjuna: “The Control Gap We Need to Address Now in Governing Agentic AI”.
Key questions
Q: What breaks when agent identity is treated as enough to authorise action?
A: The control breaks because authentication only proves the agent is known, not that the requested action is safe, reversible, or appropriate in context.
Q: Why do agentic AI systems need runtime governance instead of only pre-deployment review?
A: Because their capability set changes after deployment.
Q: How do teams decide whether an AI agent needs human approval?
A: Use the sensitivity of the action, not the cleverness of the model, as the decision point.
Practitioner guidance
- Define action-based approval boundaries Classify agent actions by reversibility, consequence, and delegated authority, then require explicit approval or block logic for irreversible steps.
- Move policy enforcement into runtime Place the authorisation decision inside the execution path so the agent cannot outpace committee review or bypass offline controls.
- Separate governance from the governed agent Run the control layer in a tamper-resistant boundary and ensure it can remain visible when it authorises, blocks, or changes policy.
Bottom line: Agentic AI creates a control problem where valid identity is not enough to justify an action, especially when the consequence cannot be easily undone.
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30/10/2026 8:53 pm
Identity is table stakes, not the control point. An agent can be fully authenticated and still act outside its intended governance boundary. That means the security programme has to move from asking whether an identity is valid to asking whether the action is acceptable in context. For identity teams, this is the point where access control and decision control diverge, and the latter becomes the harder problem.
A few things that frame the scale:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What should security teams do when agent behaviour can outpace central controls?
A: They should push enforcement closer to the workload so the decision logic is available even if a central service is slow, unavailable, or bypassed. The programme goal is to preserve control under load and degradation, because an agent that can move faster than the policy engine has already created a governance failure.
👉 Read our full editorial: The control gap in agentic AI governance is now the execution path