This course provides a practical, enterprise-focused approach to managing the risks associated with artificial intelligence systems by applying the NIST AI Risk Management Framework (AI RMF) in real-world environments. Students will develop a structured understanding of how AI systems introduce unique risks—including bias, model drift, adversarial manipulation, model theft, and lack of transparency—and how those risks differ from traditional IT and cybersecurity challenges.
The course explores how to operationalise AI governance by integrating the AI RMF with existing enterprise frameworks, including ISO/IEC 42001 Artificial Intelligence Management System and NIST Risk Management Framework SP 800-37. Learners will examine how to establish AI system inventories, classify risk, implement controls, and build governance processes that align with regulatory expectations and organisational risk tolerance. The course also introduces emerging high-assurance security concepts for AI systems, including architectural isolation, secure model handling, and advanced threat models inspired by frontier AI environments.
Through hands-on labs using open-source tools, students will assess model bias, evaluate explainability, detect model drift, and simulate real-world AI risk scenarios, including architecture design decisions around system exposure, isolation, and secure deployment models. The course emphasises not just identifying risks, but implementing measurable controls, producing audit-ready evidence, and enabling continuous monitoring of AI systems in production environments.
By the end of the course, participants will be equipped to design, implement, and operate an AI risk management program that supports secure, compliant, and trustworthy AI adoption across the enterprise.
NIST AI RMF Risk Management Training Delivery Methods
NIST AI RMF Risk Management Training Information
NIST AI RMF Risk Management Training Outline
Chapter 1: The AI Risk Landscape
- AI adoption trends across enterprise and government environments
- Differences between AI systems and traditional software systems
- AI system lifecycle: data collection, training, deployment, monitoring
- Risk amplification through scale, automation, and data dependency
- Generative AI and large language model (LLM) risk considerations
- Decision automation risks and impacts on business processes
- Ethical, legal, operational, and reputational risk categories
- Real-world examples of AI failures and unintended consequences
Chapter 2: The NIST AI Risk Management Framework
- Overview of AI RMF core functions: Govern, Map, Measure, Manage
- Establishing AI governance structures and accountability models
- Risk categorisation aligned to business and mission impact
- AI system inventory and asset management strategies
- Risk measurement techniques and qualitative vs quantitative methods
- Continuous monitoring and lifecycle risk management
- Communication of AI risk to stakeholders and leadership
- Integration of AI RMF into existing governance frameworks
Chapter 3: Mapping Traditional RMF to AI Systems
- Aligning NIST Risk Management Framework SP 800-37 with AI RMF
- Translating Prepare, Categorise, Select, Implement, Assess, Authorise, Monitor
- Categorising AI systems based on sensitivity and impact
- Selecting controls specific to AI models and data pipelines
- Implementing controls across the AI lifecycle
- Assessing AI systems for performance, fairness, and security
- Authorisation processes for AI deployment
- Continuous monitoring and reassessment strategies
Chapter 4: AI Governance and Organisational Controls
- Establishing AI governance boards and risk committees
- Defining roles and responsibilities across stakeholders
- Developing AI policies, standards, and procedures
- Model lifecycle governance and approval workflows
- Documentation requirements (model cards, data sheets, audit artifacts)
- Risk registers and accountability tracking
- Aligning AI governance with enterprise risk management (ERM)
- Preparing for regulatory and compliance requirements
Chapter 5: AI Risk Identification and Control Implementation
- Identifying AI-specific risks: bias, drift, hallucinations, adversarial threats
- Data quality and training data risk considerations
- Model asset protection, including risks related to model weights and intellectual property
- AI supply chain risks including third-party models, datasets, and dependencies
- Bias detection and mitigation strategies
- Model validation and robustness testing
- Explainability and interpretability requirements
- Technical controls vs governance controls vs operational controls
- Control mapping to risks and measurable outcomes
- Creating audit-ready evidence and documentation
Chapter 6: Explainability, Transparency, and Trust
- Importance of transparency in AI decision-making
- Black-box vs interpretable model trade-offs
- Feature importance and decision traceability
- Explainability techniques such as SHAP and LIME
- Communicating model behavior to technical and non-technical audiences
- Supporting audit, compliance, and legal requirements
- Building trust with stakeholders and end users
- Limitations and risks of explainability methods
Chapter 7: AI Security and Adversarial Risks
- Data poisoning and model poisoning attack vectors
- Adversarial machine learning techniques and evasion attacks
- Model extraction and inference attacks
- Model weight protection and risks associated with model theft and misuse
- Securing AI pipelines, datasets, and training environments
- Secure AI architecture patterns including isolation, restricted interfaces, and controlled environments
- Threat modeling for AI systems
- Integrating AI risks into existing security operations
- Detection and response strategies for AI-specific threats
- Introduction to high-assurance AI security models and emerging practices for protecting sensitive AI systems
Chapter 8: AI Monitoring, Operations, and ISO 42001 Integration
- Detecting model drift and data drift in production systems
- Monitoring performance degradation and reliability issues
- Establishing retraining triggers and lifecycle management processes
- Observability and logging for AI systems
- Overview of ISO/IEC 42001 Artificial Intelligence Management System
- Aligning AI RMF with ISO 42001 control areas
- AI risk maturity models and progression from standard controls to high-assurance environments
- Evaluating when increased isolation and restricted architectures are appropriate
- Continuous improvement and governance maturity models
- Building and sustaining an enterprise AI risk management programme
