Governance teams cannot rely only on policy documents when AI systems are operating inside real workflows. They also need evidence from the engineering and operational life of the system.
That evidence can include data-source records, model and tool rationale, evaluation results, prompt and policy configuration, monitoring logs, change records, and user training evidence.
DDAI connects governance requirements to technical implementation so assurance is supported by records from the system itself.