AI Governance and Risk

Learning Objectives Establish a practical AI governance framework without slowing adoption Evaluate AI vendors on security, privacy, and reliability Manage the key risks of enterprise AI deployment Why Governance Matters (and Why It Does Not Have to Be Painful) AI governance has a reputation problem. Many leaders hear "governance" and picture months of committee meetings, hundreds of pages of policy documents, and paralysis by analysis. That is bad governance. Good governance is lightweight, practical, and enabling — it makes AI adoption faster by removing uncertainty, not slower by adding bureaucracy. Your team needs clear answers to: "Can I use this? With what data? Do I need approval?" If the answer to every question is "let me check with legal," your governance is failing. The Minimum Viable AI Policy Every organization needs at least these five elements. You can have a working AI policy in a single page: Approved Tools List which AI tools are approved for use and at what level: Why this matters: Without a list, people either use nothing (lost productivity) or use everything (data risk). An approved list gives permission and sets boundaries simultaneously. Data Classification for AI Map your existing data classification to AI use: If you do not have data classification yet, this is a good reason to create one. It is useful far beyond AI. Human Review Requirements Define when AI output requires human review before use: External communications (client emails, marketing, public documents) — always review Internal documents (meeting notes, drafts, analysis) —…