AI Vigilance is a mechanism for continuously observing AI Systems operating in the real world, detecting and assessing Signals that emerge from their operation, and feeding the results back into Governance. An AI System does not remain fixed after Deployment. Its Context, input Data, connected Systems, user Behavior, Policy, Model, and external environment may all change over time. Evaluation performed at the time of Deployment is therefore insufficient to continuously Govern subsequent AI Operation.

Within Execution, AI Behavior, Decisions, and Actions occur in the Real-world Environment. These generate various forms of Evidence, including Operational Signals, Incidents, Outcomes, Deviation, Anomaly, Repetition, Shift, and Emerging Patterns. AI Vigilance is positioned as a function that continuously observes Evidence arising from Real-world Operation and connects Execution with Governance.

Importantly, AI Vigilance is not limited to collecting Incidents. Reactive Monitoring that responds only after a significant problem has occurred may fail to identify early indications of change. Events that appear insignificant individually may reveal new Patterns when observed across time, Systems, Users, Models, regions, or use cases. AI Vigilance must therefore continuously observe not only individual events, but also their aggregation, repetition, change, and concentration.

The basic flow of AI Vigilance consists of Monitor → Detect → Assess → Feed Back. Operational Evidence is first Monitored to Detect Deviations from normal conditions or emerging Signals. Those Signals are then Assessed to determine whether they represent incidental variation or require additional Evaluation or Governance Action. Where necessary, the results are fed back to the Governance side and connected to appropriate Review or Escalation.

AI Vigilance, however, does not independently modify Governance. Its role is to observe Evidence, detect and assess Signals, and connect relevant findings to appropriate Review or Escalation. The review or update of Policy, Authority, Process, Constraints, or other Governance elements is performed in accordance with established Authority and Process.

AI Vigilance is therefore not a standalone Monitoring Tool for observing AI. It is a Continuous Governance Mechanism that connects Governance and Execution through Real-world Evidence and returns insights derived from Operation back into Governance. Through this mechanism, Governance does not remain a static design established at Deployment, but becomes a continuous cycle in which real-world change can be observed and Governance can be reviewed on the basis of verifiable Evidence.