This course teaches learners how to analyze, evaluate, and systematically manage risks in AI projects. Learners explore technical, regulatory, and operational risks across the system lifecycle, from data collection to deployment and monitoring. They practice comparing mitigation strategies using structured tradeoff frameworks that weigh cost, timeline, and effectiveness. Hands-on activities include facilitating a SWIFT session to surface data-privacy risks, evaluating privacy-preserving techniques, and configuring tools like Jira to track risks automatically. Learners also build and submit a sample risk register that scores, prioritizes, and documents risks with clear ownership and mitigation plans. By the end, learners will confidently identify and manage AI risks, apply structured frameworks to real-world projects, and create practical documentation that strengthens accountability, compliance, and decision-making in AI initiatives."