AI Governance.

The policies, processes, roles, and controls organisations and regulators use to ensure AI systems are developed, deployed, and operated responsibly and lawfully. It belongs to the AI & ML Security category of cybersecurity.

The policies, processes, roles, and controls organisations and regulators use to ensure AI systems are developed, deployed, and operated responsibly and lawfully.

AI governance translates ethical principles and legal requirements into concrete controls: model inventories, risk classification, impact assessments, approval gates, transparency, monitoring, and accountability. Reference frameworks include the NIST AI Risk Management Framework (AI RMF) and its 600-1 Generative AI profile, ISO/IEC 42001 management-system standard, OECD AI Principles, the EU AI Act, and the UK and US AI Safety Institute evaluations. Governance functions span legal, compliance, security, privacy, ML engineering, and product. Mature programs maintain an AI Bill of Materials, conduct red-team and bias evaluations, log all production model versions, and provide structured incident-response and audit capabilities to satisfy regulators and customers.

Defences for AI Governance typically combine technical controls and operational practices, as detailed in the full definition above.

Common alternative names include: AI risk management, Responsible AI governance.