TLDR:
AI governance is the set of policies, processes, and oversight mechanisms that organizations and governments use to ensure AI systems are developed and deployed responsibly. It spans organizational governance (board oversight, internal policies, risk management) and external regulation (the EU AI Act, sectoral regulations, voluntary frameworks).
Organizational AI Governance Components
Mature organizational AI governance typically includes: an AI risk management framework (often based on NIST AI RMF or ISO/IEC 42001), board-level AI oversight committee with clear escalation paths, AI inventory and risk classification (mapping all AI systems against applicable risk categories), procurement and vendor management standards (third-party AI due diligence), employee training and acceptable use policies, model card requirements for all production AI, evaluation and monitoring infrastructure, incident response plans for AI failures, and external transparency reporting.
Standards and Frameworks
Key reference frameworks include: NIST AI Risk Management Framework (US, voluntary, comprehensive), ISO/IEC 42001 (international management system standard for AI), OECD AI Principles (high-level governance principles adopted by 40+ countries), the EU AI Act (binding regulation), the UK Pro-Innovation Approach (principles-based), and sector-specific guidance from financial regulators (Basel Committee, ECB), healthcare regulators (FDA, EMA), and labor authorities (EEOC AI hiring guidance).
Practical Implementation
For startups and enterprises, AI governance implementation typically proceeds in stages: first, establishing an AI inventory and assigning accountability for each system; second, classifying systems by risk and identifying applicable obligations; third, implementing technical and process controls proportionate to risk; fourth, building monitoring and reporting infrastructure. The investment scales with risk exposure—a company deploying generative AI for marketing has different obligations than one deploying AI in healthcare diagnosis or financial credit decisions. Boards increasingly expect quarterly AI governance reporting alongside cybersecurity and privacy reporting.