Manage AI Risk Before It Manages You: NIST AI Risk Management Framework

AI has already spread through your organization—often faster than anyone intended. Use the NIST AI Risk Management Framework to govern, map, measure, and manage AI, and produce a defensible, prioritized risk response for the board or regulator.
  • Author: Dr. Zitianellis
  • Level: Foundational
  • Study Mode: Self-paced
  • Recommended Period: 2 Weeks
  • Estimated Learning Time: ~14 Hours
  • Proficiency: 2 Graded Assessments
  • Award: 1 Certificate
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To ensure the best continuity in your learning, we recommend completing the course within the recommended completion period. The course is self-paced, so you can adapt your learning schedule around work and other commitments.
Course overview
"Manage AI Risk Before It Manages You" is a two-week micro-course designed for executives on the NIST AI Risk Management Framework. It focuses on a practical approach to managing AI use within an organization through four key functions: Govern, Map, Measure, and Manage.
Participants select one AI system to analyze throughout the course, learning to establish governance and accountability, map risks and benefits, measure trustworthiness, and prioritize significant risks. No prior AI knowledge is required. By the end, you'll have a defended, prioritized risk response for your chosen system, ready to present to a board or regulator.
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  • Artificial Intelligence (AI)  
  • Risk Management  
  • AI Governance  
  • NIST AI RMF

Your Instructor

Taught by a practitioner. Moderated by the same.
PROFESSOR OF DATA SCIENCE
Dr. Janine Zitianellis is an applied data scientist and responsible-AI specialist with professional experience across financial services, retail, supply chain, operations and pharmaceutical manufacturing. She has developed practical solutions in forecasting, segmentation, anomaly detection, data quality, credit analytics and decision support for organizations including SubjectWell, IQbusiness, PBT Group, Sanlam and Standard Bank Group. Her research integrates AI, machine learning, behavioral science and ethical data use. At Monarch, she directs the Doctor of Applied Behavioural Data Science and supports curriculum and instructional design, helping MMI participants translate complex data into responsible, evidence-based managerial action.