Runtime AI governance evaluates context, policy, and risk while an AI interaction or agent action is taking place, allowing an organization to observe, allow, block, redact, or require approval before or during execution.
This is an operational definition. Available interventions depend on a control point that can affect the relevant interaction or action. Evaluation can produce a decision without establishing that an executor applied it. The term does not promise every intervention in every integration.
What makes a decision relevant at runtime
A runtime decision applies a policy to a particular request using context available while the workflow operates. The operation, target, acting identity, and policy conditions determine what needs to be established. A rule written in advance still needs an application point in the execution path.
The context can change without the rule changing. An approval may no longer apply to a revised target, or a permission may depend on a condition that no longer holds. The implementation needs a defined way to determine whether the decision remains valid for the operation that follows.
Evaluation and application are different responsibilities
Evaluation determines an outcome under the policy. An effective control connects that outcome to what the executor may do. Observation can supply decision context or record activity, but recording a decision does not itself establish that execution depended on it.
The OWASP Authorization Cheat Sheet recommends validating permissions on each request. That captures an access-control principle relevant here; it is not a complete definition or architecture for runtime AI governance.
Specify what happens when required context is missing, evaluation fails, or approval remains pending. A system that continues on a failed evaluation has a different control boundary from one that withholds the operation. Neither behavior should be inferred from the category name.
What the term does not guarantee
Runtime does not mean that an intervention can undo an earlier effect. A decision to stop a later step cannot retract information already disclosed. Likewise, permission to attempt an operation does not establish that it succeeded; an outcome can remain unknown when evidence is insufficient.
Related concepts
- AI agent governance defines the broader authority, policies, controls, and evidence governing agents.
- Pre-execution governance focuses on evaluating an operation while a control can still withhold the effect being governed. It identifies a particular boundary within the broader runtime discussion.
The runtime AI governance pillar develops the context, validity, application, failure, and evidence contracts behind these distinctions.