AI Risk: Analyze, Evaluate, Register
About this course
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.
63/100
CourseAsk score
- What the provider tells you
- 39/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- analyze technical and operational risks in AI
- evaluate regulatory challenges in AI projects
- practice risk mitigation strategies using structured frameworks
- create and manage a risk register
Course objectives
- equip learners to systematically manage AI risks
- develop skills for evaluating privacy-preserving techniques
- enable participants to document risks with clear ownership and mitigation plans
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