The role of AI Vigilance is not limited to monitoring individual AI Systems. It continuously analyzes Evidence generated through Real-world Operation and connects the results to different Governance Actors, including Organizations, AI Providers, Regulators, and Professional Communities. For AI Governance to function continuously, insights derived from operation must be fed back to the appropriate Governance Level.

Within Organizations and AI Providers, Evaluation, Policy, Authority, Context, and other elements form the Governance State. Execution Evidence and other Operational Evidence generated through actual operation are observed and analyzed by AI Vigilance, allowing Signals to be detected across multiple Systems, Decisions, Incidents, and Outcomes. These include changes such as Deviation, Anomaly, Repetition, Shift, and Emerging Pattern, which may become visible only through continuous observation.

When a detected Signal can be addressed within an individual Organization, the relevant Evidence and Analysis are fed back into its Governance System and connected to Evaluation or Review. This may lead to the review of Policy, Process, Constraints, or other elements under the appropriate Authority and Process. Signals related to an AI Provider’s Model, Service, or Operational Policy are similarly connected to the Provider’s Governance Process.

Issues shared across multiple Organizations or Providers, issues not adequately addressed by existing Rules, or issues requiring a public response are connected to broader Governance Actors. Relevant Evidence and Analysis may be Reported to Regulators and used to inform Measures, Guidance, or Rules. Where specialized judgment is required, expertise and Evaluation Criteria from Domestic / International Professional Communities can also be incorporated into the Governance Process.

The Translation Hub transforms Law, Regulation, Standards, Professional Guidance, and other forms of Governance into Machine-readable Governance Structure. When Real-world Operation reveals Ambiguity, Gaps, or Conflicts in existing Governance Requirements, Evidence can be returned to the appropriate Governance Actor for Clarification or Review. If Governance is subsequently changed, the result is reflected in the Machine-readable Structure through the Translation Hub.

Importantly, AI Vigilance does not itself become a Regulator or Governance Authority. It observes and analyzes Evidence, detects Signals, and connects them to the appropriate Governance Actors. Authority to establish or modify Governance remains with legitimately authorized actors.

AI Vigilance therefore functions as a circulation mechanism that returns Evidence from Execution to Governance and connects different Governance Loops across Organizations, Providers, Professional Communities, and Regulators. Through this structure, AI Governance becomes a Governance Ecosystem in which Real-world Evidence circulates to the appropriate Governance Levels, enabling continuous Review and Update.