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18/08/2026 2:09 pm
TL;DR: Agentic AI governance is about controlling what autonomous agents can do, not just judging their output, according to Ory; the article lays out five control pillars, from identity and least privilege to runtime enforcement and auditability. The central failure is assuming traditional AI governance can contain action-taking systems that call APIs, move data, and trigger workflows on their own.
NHIMG editorial — based on content published by Ory: What Is Agentic AI Governance and How to Implement It
By the numbers:
- 98% have deployed or planning to deploy AI agents, but 79% of those organizations lack written policies for governing them.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams implement runtime controls for AI agents in enterprise environments?
A: Start by enforcing policy at the point where the agent requests access, not only where the data lives.
Q: Why do AI agents change IAM and PAM assumptions?
A: AI agents change IAM and PAM assumptions because they can act continuously, use tools directly, and execute without the human pacing that traditional review cycles expect.
Q: What breaks when agents rely on shared credentials or borrowed user identities?
A: Auditability breaks first, followed by recertification and containment.
Practitioner guidance
- Define each agent’s delegated authority Document the exact tasks, tools, workflows, and autonomy level each agent is permitted to use before deployment.
- Assign a unique identity to every agent Issue separate machine credentials per agent so traceability, revocation, and forensic review remain actor-specific.
- Enforce policy at the API boundary Place authorization controls in an identity-aware proxy or equivalent enforcement layer that sits in front of services the agent calls.
What's in the full article
Ory's full article covers the operational detail this post intentionally leaves for the source:
- The five-pillar implementation model with more detail on identity, policy, oversight, and monitoring choices
- Examples of runtime authorization patterns for agents calling APIs and handling sensitive actions
- The article’s discussion of Ory Hydra, Keto, and related identity components in the governance stack
- The standards references and product-specific implementation guidance that support the framework
👉 Read Ory's guide to implementing agentic AI governance →
Agentic AI governance: are your runtime controls keeping up?
Explore further
18/08/2026 3:02 pm
Agentic AI governance is a runtime authorization problem, not a model quality problem. Traditional AI governance controls output quality, bias, and review. Those controls do not stop an autonomous system from invoking an API, moving data, or triggering a workflow it was never authorized to use. The implication for practitioners is that governance must move to the action boundary, where identity and policy decide what the agent can do in real time.
A few things that frame the scale:
- 98% have deployed or planning to deploy AI agents, but 79% of those organizations lack written policies for governing them, according to AI Agents: The New Attack Surface report.
- Only 44% of organisations have implemented any policies to govern AI agents, which shows how quickly deployment can outrun policy.
A question worth separating out:
Q: Who is accountable when an AI agent takes an unsafe action?
A: Accountability should sit with the business owner of the agent, the team that provisioned the access, and the control owners responsible for monitoring and revocation. If no one can answer who approved the identity, the scope, and the oversight model, the governance framework is not complete enough for production.
👉 Read our full editorial: Agentic AI governance requires runtime control, not output review
