Policy & Governance
AI Governance
The policies, processes, and controls an organization uses to manage AI adoption and risk.
AI governance encompasses the full set of organizational structures, policies, workflows, and technical controls used to oversee how AI is adopted, used, and retired within an enterprise. It covers the entire lifecycle: from discovering what AI tools exist, to evaluating and approving new ones, to monitoring ongoing usage, to proving compliance to regulators and leadership.
Unlike traditional IT governance, AI governance must account for unique risks: model behavior that changes over time, vendor data handling practices that differ from conventional SaaS, embedded AI features that activate without explicit adoption decisions, and regulatory frameworks (like the EU AI Act) that impose specific obligations on AI deployers.
Why it matters
Without governance, AI adoption becomes reactive — security teams chase down tools after incidents rather than evaluating them proactively. A structured governance program turns AI adoption from a risk into a competitive advantage by letting teams move fast with confidence.