For Reports and Evaluations generated through AI Vigilance to function as Governance Evidence, it is necessary to define the Standards and Procedures by which their contents can be independently verified. Even where Vigilance operates continuously, it cannot provide a foundation for Audit / Assurance unless the validity of its activities and Findings can later be verified.

In established Audit / Assurance domains, the methods for determining what constitutes Evidence, how it should be evaluated, and what Records should be preserved have been institutionalized over long periods. Financial Audit, for example, is built on common monetary units and Accounting Rules, supported by standardized Evaluation Methods, Professional Practice, Recordkeeping, and Audit Trails. In the Pharmaceutical domain, although evaluation targets differ across Products and Processes, verifiability is supported through Measurement Criteria, SOPs, controlled Source Data, and Audit / Inspection Frameworks.

AI Vigilance operates under more variable conditions. What constitutes an important Signal or an acceptable Outcome may differ according to the AI System, Use Case, Risk, User, Domain, Region, Culture, and Regulatory Environment. The same Behavior may have different Governance Significance depending on Context. It is therefore difficult to evaluate all AI Vigilance through a single common metric, and complete uniformity of Evaluation Criteria would not necessarily be appropriate.

However, Context-dependent Evaluation does not mean that Verification is impossible. What should be standardized is not necessarily the Evaluation Conclusion itself, but the Evidence Process through which supporting Evidence is generated, preserved, associated, and maintained in a verifiable form.

Vigilance Reporting Criteria define what information is Reportable. DSE preserves the State at the time of a Decision, while Provenance enables the Origin and modification history of Evidence to be traced. Governance State provides the Governance Context at the relevant time, Event / Execution Records support verification of what actually occurred or was executed, and Audit Trails trace subsequent Evaluation, Escalation, Reporting, and Response.

This allows later verification of what Evidence was relied upon, which Criteria were applied, what Process was followed, and how a Finding or Conclusion was reached, even where Evaluation Criteria differ across Organizations, Domains, or Regions. Assurance does not require every judgment to reach the same conclusion. It requires each judgment to be verifiably grounded in a defined Governance Process and supporting Evidence.

AI Vigilance therefore does not require different Contexts to be forced into a single common metric. The more Context-dependent the Evaluation, the more important the standardization of Evidence Generation, Recordkeeping, Traceability, Verification, and Assurance Procedures becomes.

This common Evidence Foundation enables independent third-party verification of AI Vigilance operating across different Contexts and provides the basis for future AI Vigilance Audit / Assurance Standards.