AI Governance
The framework of policies, processes, and controls that ensure AI systems operate safely, ethically, and in compliance with regulations.
Definition
AI governance encompasses the organizational structures, policies, workflows, and technical controls that enable businesses to develop, deploy, and operate AI systems responsibly. Unlike adjacent concepts such as AI safety (which focuses on research into preventing AI harms) or responsible AI (which emphasizes ethical principles), AI governance is fundamentally operational: it defines who can approve AI decisions, what documentation must exist, how oversight is maintained, and what evidence is captured for accountability.
The EU AI Act establishes the world's first comprehensive regulatory framework for artificial intelligence, and governance sits at its core. High-risk AI systems under the regulation must demonstrate robust governance through documented risk management systems (Article 9), quality management processes (Article 17), and human oversight mechanisms (Article 14). Without a coherent governance framework, organizations cannot meet these requirements systematically. The August 2026 deadline for high-risk AI system compliance means that governance structures must be operational well before that date, not designed reactively in response to audit findings.
Implementing effective AI governance requires three interconnected layers. First, organizations need policy frameworks that define acceptable use cases, risk thresholds, and approval requirements for AI systems. Second, they need operational workflows that enforce these policies at decision-time, including approval queues, escalation paths, and override procedures. Third, they need technical infrastructure that captures evidence of governance in action: audit trails showing who approved what, when, and why.
Many organizations already have governance frameworks for other domains (data privacy, financial controls, IT security), but AI governance presents unique challenges. AI systems make decisions at scale and speed that exceed traditional review processes. They evolve over time as models drift or are updated. And they often operate as components within larger workflows, making accountability harder to trace.