AI Model Risk Management

AI models create value, but they also create risks — from data drift and bias to regulatory non-compliance. In this short, practical course, you’ll learn how to make those risks visible, measurable, and governable. First, you’ll explore the main categories of model risk and practice mapping them to governance controls and KPIs. Next, you’ll learn how to evaluate model validation results against standards such as SR 11-7, the Basel Principles, and the EU AI Act, identifying compliance gaps and recommending corrective actions. Finally, you’ll draft a simple model-risk control framework with clear documentation standards, escalation paths, and review cadences. By the end, you’ll be able to demonstrate governance skills that help organizations deploy AI responsibly and maintain trust

• Identify and categorize AI model risks
• Map model risks to governance controls and KPIs
• Evaluate validation results against regulatory standards
• Develop a basic model risk management framework

• Basic familiarity with AI or risk management concepts
• Willingness to engage in applied, case-based exercises