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
Write your awesome label here.
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.
Empty space, drag to resize
  • Artificial Intelligence (AI)  
  • Risk Management  
  • AI Governance  
  • NIST AI RMF

Your Instructor

Taught by a practitioner. Moderated by the same.
Empty space, drag to resize
Dr. Zitianellis brings broad data science and analytics experience across financial services, retail, supply chain, operations, and pharmaceutical manufacturing. She has worked with SubjectWell, IQbusiness, PBT Group, Sanlam, and Standard Bank Group, delivering forecasting, segmentation, anomaly detection, data quality, credit analytics, and reporting solutions.
At Monarch, she is Director of the Doctor of Applied Behavioral Data Science program and supports curriculum development, instructional design, and program operations across Bachelor, Master, and Doctoral programs.
As an educator, she contributes to academic and executive data education. She is Head Tutor for the Economist Education course Data Storytelling and Visualization, supporting data communication and analysis. Currently assesses work for the University of Cape Town Data Skills Accelerator program, focusing on fair evaluation, feedback and student outcomes.
Dr. Zitianellis holds a Doctor of Business Administration and M.Phil. in Business Research from Monarch Business School Switzerland, an MSc in Data Analytics and Operations Management from Arden University, and a postgraduate diploma in Data Science and Business Analytics from the University of Texas at Austin.
Her doctoral work examined AI and machine learning to improve clinical trial patient recruitment, integrating health behaviour theory, NLP and predictive modelling. Broader interests include ethical data analysis, machine learning in business, data visualisation, operations and supply chain analytics, risk analysis and mixed-methods research. 
She is committed to ethical data use, affiliated with the Algorithmic Justice League’s Agents for Change, and supports animal welfare and education community projects. Her research covers big data analytics and supply chain management, as well as AI/ML applications in clinical trial recruitment. She has presented at the Applied AI Summit and the 21st Century Women’s Entrepreneurship and Leadership Conference.