This course is for learners who need to understand, build, or oversee AI governance and compliance programs. It begins by turning governance design into practice: building accountability structures, setting acceptance criteria, error tolerance, and risk tolerance, and addressing shadow AI with the NIST AI RMF. It then covers the audit function, including audit types, model drift, EU conformity assessments, U.S. state audit expectations, and the audit trail. The course closes with the philosophical debate between the precautionary principle and the innovation principle, tracing it through history, U.S. federal and state policy, the EU AI Act, agentic AI, and real-world cases. Throughout, it emphasizes that effective governance should produce adherence rather than avoidance.