Who should own runtime authorization policy for AI agents?

Why This Matters for Security Teams

runtime authorization for AI agents is not just an IAM question. Agents act on goals, not fixed job roles, so the policy owner has to understand entitlement, tool exposure, and downstream enforcement together. That is why ownership cannot stop at the gateway. If application owners do not honor the same decision, the policy is cosmetic. Current guidance suggests treating runtime authorization as a shared control surface across IAM, IGA, PAM, and application teams, with clear accountability for each layer.

The risk is already visible in agentic environments. NHIMG’s AI LLM hijack breach coverage and the OWASP NHI Top 10 both show that the control failure is usually not one missing approval. It is the mismatch between who defines policy, who issues credentials, and who can actually enforce limits at runtime. NIST’s NIST AI Risk Management Framework is useful here because it frames governance, measurement, and monitoring as continuous responsibilities rather than one-time setup tasks.

In practice, many security teams encounter policy gaps only after an agent has already accessed a tool or dataset outside its intended scope, rather than through intentional governance design.

How It Works in Practice

Ownership should be assigned by control function, not by whichever team owns the first checkpoint. IAM typically owns identity proofing, token issuance, and baseline authentication. IGA owns entitlement governance, recertification, and role-to-access mapping. PAM owns privileged session and secret handling for high-risk actions. Application owners own enforcement in the workload, which is where runtime authorization either succeeds or fails.

For AI agents, that means policy should be evaluated at request time using the current task, context, and destination tool. Static role-based access is too blunt for autonomous systems because an agent’s next action is not always predictable. Best practice is evolving toward intent-based authorization, short-lived credentials, and workload identity so the system can verify what the agent is trying to do before it is allowed to do it. That pattern aligns with the OWASP Agentic AI Top 10 and the CSA MAESTRO agentic AI threat modeling framework, both of which emphasize runtime context and policy enforcement over static assumptions.

• IAM defines the identity primitive, often using workload identity rather than a human-centric account model.
• IGA validates whether the requested access is still within approved business intent.
• PAM limits privileged actions, secret exposure, and elevated session scope.
• Application owners enforce the final allow or deny decision inside the tool, API, or workflow.

NHIMG’s Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs is useful for mapping this ownership across the NHI lifecycle, while the Top 10 NHI Issues highlights what happens when secrets, scopes, and enforcement drift apart. These controls tend to break down when the gateway and the downstream service are owned by different teams and the application ignores the gateway decision because there is no shared policy contract.

Common Variations and Edge Cases

Tighter runtime control often increases coordination overhead, requiring organisations to balance stronger enforcement against delivery speed. That tradeoff becomes sharper in multi-agent systems, where one agent can call another, chain tools, or trigger workflows in services owned by different teams. There is no universal standard for this yet, so ownership models vary. Some organisations centralize policy definition in a security platform team, while others keep policy authorship close to the application and require security sign-off.

Edge cases usually appear where enforcement is fragmented. If an agent uses a brokered API, the broker may enforce authorization, but the downstream service must still validate the request if it has independent privileges. If a developer embeds local allowlists in code, that can help, but it does not replace centralized review or auditability. For high-risk actions, best practice is to combine real-time policy evaluation with short-lived credentials and explicit privilege boundaries, as reflected in the NIST Cybersecurity Framework 2.0 and the NIST AI Risk Management Framework.

NHIMG’s OWASP Agentic Applications Top 10 and the Ultimate Guide to NHIs — Regulatory and Audit Perspectives both reinforce the same operational point: governance fails when policy is defined in one layer and enforced, or ignored, in another.

Related resources from NHI Mgmt Group

• How should organizations approach the governance of AI agents?
• When should organisations use runtime authorization for AI agents?
• Why do service accounts and AI agents increase the need for runtime authorization?
• Who should own policy for AI coding agents in production?