One real AI system
Apply the framework to a specific use rather than discuss AI risk in the abstract.
Monarch Management Institute · Micro Course
Manage AI Risk Before It Manages You – Implementing the NIST AI Risk Management Framework
Bring a real AI use under control before it becomes a board, regulatory or reputational crisis. Build the executive confidence to govern AI risk, demand credible evidence and defend the actions your organisation takes.
EnrollRegistration and course delivery take place in the MMI LMS.
Course introduction
See what this course makes possible.
Meet the course lead and explore the practical focus, learning experience, and professional value of this course.
Enroll in this course →Course overview
About This Course
Most organisations did not make a single, deliberate decision to adopt artificial intelligence; it arrived gradually. A vendor introduced a feature, a team began using a public chatbot, or a supplier quietly embedded a model in a workflow. By the time leadership asks who owns the risk, AI may already be influencing decisions that affect customers, employees and the balance sheet—and it can fail in ways ordinary software does not.
This course closes the gap between saying that an organisation “uses AI responsibly” and being able to demonstrate responsibility for a specific system. Participants take one real AI use through the full NIST AI Risk Management Framework cycle: Govern, Map, Measure and Manage. Each stage is demonstrated through a running executive case and then applied to the participant’s chosen system, with a model against which the work can be tested.
By the conclusion, participants hold a defensible risk response: named accountability and risk tolerance, a priority risk and the people it may affect, evidence of what has been measured and what remains uncertain, and a prioritised treatment supported by ownership, third-party terms, incident planning and monitoring. The emphasis is on managerial judgement and practical control rather than technical model development or regulatory box-ticking.
Why this course is different
One real AI use, governed through a credible practitioner framework.
Framework as judgment
Use NIST AI RMF to organise evidence and decisions, not as a compliance checklist.
Written for accountable executives
Translate technical risk into ownership, treatment, monitoring and defensible action.
Learning objectives
- Explain the purpose and structure of the NIST AI Risk Management Framework.
- Establish governance, accountability and risk tolerance for an AI use.
- Map system context, stakeholders, impacts and third-party dependencies.
- Select meaningful trustworthiness measures and evidence.
- Prioritise risk treatment and residual-risk decisions.
- Design monitoring, escalation and incident-response arrangements.
Practical outcomes
- A defined AI use and governance baseline.
- A clear accountability and risk-tolerance statement.
- A mapped set of stakeholders, impacts and dependencies.
- A focused evidence and measurement plan.
- A prioritised AI risk treatment response.
- A monitoring and incident-response framework for executive oversight.
MONARCH PRACTICE TOOLKIT
Turn knowledge into practical capability.
Every MMI course includes a course-specific interactive Practice Toolkit—original Monarch intellectual property developed by Monarch faculty to support its learning objectives. Use the toolkit to apply key concepts, practise professional judgement and produce work you can carry into practice.
Discover Monarch Practice Toolkits →Modules at a glance
Module 01Govern and Map AI RiskDuration = 1 Week
Establish governance, accountability and risk tolerance, then map the context, stakeholders, dependencies, risk categories and potential impacts of one real AI use.
Principal themes
- Governance, accountability and ownership
- Risk tolerance, inventory and third parties
- System context, stakeholders and impacts
Intended outcomes
Produce a clear governance and context baseline for one real AI use.
Module 02Measure and Manage AI RiskDuration = 1 Week
Select credible trustworthiness measures and evidence, monitor performance and drift, prioritise treatment, and design escalation and incident-response actions.
Principal themes
- Trustworthiness characteristics, metrics and TEVV
- Monitoring, drift and evidence quality
- Risk treatment, residual risk and incident response
Intended outcomes
Produce a prioritised, evidence-based AI risk response with monitoring and accountability.
Dr. Zitianellis
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 organisations including SubjectWell, IQbusiness, PBT Group, Sanlam and Standard Bank Group. Her research integrates AI, machine learning, behavioural 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.