governance

Explores how organizations investigate AI incidents—from model failures and bias to security breaches. Outlines detection, evidence collection, and root-cause analysis across data, model, and deployment layers. Emphasizes documentation, stakeholder communication, and corrective actions, embedding lessons into governance to reduce risk and improve model reliability.

Explains how AI streamlines regulatory compliance by automating control mapping, continuous monitoring, evidence collection, and reporting. Covers data governance, privacy by design, model risk, and audit trails, with references to GDPR, HIPAA, PCI DSS, and SOX. Highlights benefits such as lower cost and risk, and best practices like human oversight, explainability, testing, and vendor due diligence.