AI Fundamentals for GRC Professionals

The AI-GRC Foundations course is structured to build a strong conceptual understanding of artificial intelligence governance before moving into hands-on implementation. The curriculum begins with AI fundamentals, introducing students to core concepts such as artificial intelligence, machine learning, structured vs unstructured data, model types, and the difference between deterministic and probabilistic systems. It then transitions into AI risk fundamentals, where students explore how AI risk differs from traditional IT risk. Topics include bias, model drift, explainability challenges, automation risk, lifecycle risk points, and structured AI risk categories. The course then establishes AI governance foundations, covering oversight structures, roles and responsibilities, human-in-the-loop controls, transparency requirements, and accountability mechanisms within organizations. Next, students are introduced to the global AI regulatory landscape, including major frameworks and standards shaping AI compliance expectations. The curriculum explains how traditional GRC frameworks extend to AI systems and what regulators expect in high-risk AI environments. Finally, the course prepares students for practical implementation by outlining the real responsibilities of an AI-GRC analyst, introducing model inventory concepts, and identifying the skills required before conducting AI risk assessments or governance design. This curriculum ensures students develop structured thinking, regulatory awareness, and risk analysis capability before entering advanced AI-GRC hands-on training.