model risk management

Explores why AI risk assessments are crucial and how to run them. Covers scoping use cases, data mapping, model performance and bias checks, privacy and security reviews, legal and ethical impacts, and supply chain risks. Recommends controls - testing, explainability, human oversight, and incident response - plus continuous monitoring, documentation, and alignment with NIST/ISO and the EU AI Act.