AI Governance
AI governance is the set of controls that lets an organization deploy AI responsibly: knowing what AI systems are running, bounding what they can do, logging what they did, and naming who's accountable. It's how you earn the right to ship AI that takes real actions.
AI governance is what turns “we built an AI system” into “we can responsibly run it.” In practice it’s four things: an inventory of every model and agent in production, controls that limit what each can access and do, audit trails that record decisions and actions, and a named owner accountable for each system. Together they make AI behavior visible, bounded, and reviewable.
It matters most for systems that take real actions or touch sensitive data, where a wrong move is a serious liability and most leaders are right to be cautious about trusting outputs. Done as an afterthought, governance is a blocker; done as engineering — permissions, logging, and ownership built in — it’s what lets an organization widen an AI system’s scope over time instead of freezing it.