Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

The NIST AI Risk Management Framework (AI RMF 1.0) has become a global benchmark for practical, lifecycle-wide AI risk governance. This course teaches risk, governance and product teams how to translate the framework's seven trustworthiness characteristics and four core functions—Govern, Map, Measure and Manage—into daily enterprise workflows.

Participants work with the NIST AI RMF Playbook subcategories, context mapping (Map), quantitative and qualitative TEVV practices (Measure), risk response and monitoring (Manage), and Current-to-Target Profile gap analysis. In the workshop, teams construct a NIST AI RMF Profile and action plan for one of their own AI systems.

Who Should Attend?

  • Enterprise Risk Management (ERM), operational risk and Model Risk Management (MRM) teams
  • AI governance leads, CISO/cyber governance managers and compliance officers
  • Technology, finance and enterprise leaders aligning with global NIST standards
  • AI quality assurance, evaluation and risk validation specialists

Key Takeaways

  • Apply NIST's seven characteristics of trustworthy AI and manage trade-offs among them
  • Embed the Govern, Map, Measure and Manage functions into the AI project lifecycle
  • Structure TEVV (Testing, Evaluation, Verification and Validation) activities across lifecycle stages
  • Select and tailor actionable controls from the NIST AI RMF Playbook
  • Build a Current Profile and Target Profile to prioritise risk-reduction investments

Curriculum

NIST AI RMF 1.0 Architecture and the Seven Trustworthiness Characteristics
  • Valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed
  • Managing trade-offs across characteristics (e.g., privacy vs accuracy, speed vs explainability)
  • How NIST AI RMF complements ISO/IEC 42001 and the EU AI Act
GOVERN and MAP: Organisational Foundation and Context Framing
  • GOVERN (1–6): Policies, risk tolerance, accountability structures, workforce diversity and third-party oversight
  • MAP (1–5): framing intended context, impacted stakeholders, technical capabilities and foreseeable harms
  • Using Map outputs to drive early-stage go/no-go decisions
MEASURE: TEVV Practices, Metrics and Independent Review
  • MEASURE (1–4): Selecting qualitative and quantitative metrics for identified AI risks
  • Designing Testing, Evaluation, Verification and Validation (TEVV) and red-teaming cadences
  • Documenting measurement limitations and residual uncertainty
MANAGE: Risk Treatment, Monitoring and Decommissioning
  • MANAGE (1–4): Prioritising and responding to risks (mitigate, transfer, share, accept or decommission)
  • Continuous post-deployment monitoring, emergent risk handling and incident recovery
Workshop: Using the NIST Playbook to Build a Target Profile
  • Scoring the Current Profile and defining the Target Profile for an internal use case
  • Selecting Playbook actions for a 90-day implementation roadmap

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