A tailored course, built for your situation
Deeper command of the NIST AI Risk Management Framework
Name the patterns, map the controls, lead the review, without deferring to external guidance
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
The situation this course is for
Who this is for
Senior practitioner in AI governance or risk advisory at a federal consulting firm, regularly engaged in client assessments, framework implementation, or policy translation work
Who this is not for
Entry-level analysts, auditors focused solely on compliance checklists, or practitioners not involved in shaping AI governance deliverables
What you walk away with
- Map AI risk scenarios directly to NIST AI RMF subcategories with confidence
- Explain control rationale using authoritative sources and real-world analogs
- Produce clear, client-facing assessment summaries anchored in the framework
- Anticipate reviewer questions and address them preemptively in documentation
- Adapt the framework to novel use cases without losing alignment with intent
The 12 modules (with all 144 chapters)
Module 1. Core anatomy of the NIST AI RMF
Break down the framework’s four functions, Map, Measure, Manage, Govern, and understand how they interact in real client engagements. Learn to distinguish mandatory anchors from flexible implementation paths.
12 chapters in this module
- What the framework is designed to solve
- How Map differs from Measure in practice
- Govern function triggers and thresholds
- Managing third-party AI risk within the model
- Mapping organisational roles to functions
- When to escalate beyond the framework
- Key differences from ISO/IEC 42001
- AI life cycle alignment points
- Integrating with existing risk registries
- Handling dual-use foundation models
- Common misinterpretations to avoid
- Building your first framework snapshot
Module 2. Map function: Scoping AI systems
Master the art of defining AI system boundaries, identifying stakeholders, and cataloguing impacts. Use proven heuristics to avoid over- or under-scoping client systems.
12 chapters in this module
- Defining what counts as an AI system
- Stakeholder mapping technique
- Impact dimension selection
- Handling classified or sensitive systems
- Identifying downstream dependencies
- Boundary setting for generative AI
- Dealing with legacy integrations
- Using data provenance to scope
- Documenting rationale for review
- Aligning scope with client objectives
- Managing edge cases in federal contexts
- Template for scoping memorandum
Module 3. Measure function: Risk assessment methods
Apply quantitative and qualitative techniques to assess AI risks, including scoring models, scenario analysis, and stakeholder validation. Learn to justify severity ratings with traceable logic.
12 chapters in this module
- Selecting appropriate risk metrics
- Scenario development process
- Scoring likelihood and impact
- Validation with non-technical stakeholders
- Weighting fairness, safety, security
- Benchmarking against peer systems
- Using historical incident data
- Handling low-probability high-impact risks
- Documenting measurement uncertainty
- Integrating human oversight factors
- Adapting for national security systems
- Worked example: facial recognition system
Module 4. Manage function: Controls and mitigations
Match identified risks to specific controls from the framework and complementary sources. Build layered mitigation strategies that go beyond checkbox compliance.
12 chapters in this module
- Control selection decision tree
- Mapping risks to subcategories
- Layering technical and procedural controls
- Using NIST SP 800-53 crosswalks
- Customising controls for mission needs
- Third-party validation strategies
- Monitoring effectiveness over time
- Handling irreversible harms
- Documentation requirements per tier
- Mitigation trade-off analysis
- Escalation paths for unresolvable risks
- Template: mitigation action plan
Module 5. Govern function: Oversight and accountability
Structure governance mechanisms that ensure ongoing compliance, ethical review, and adaptation. Design roles, cadences, and escalation protocols tailored to client environments.
12 chapters in this module
- Defining governance bodies
- Establishing review cadences
- Role clarity for AI stewards
- Ethics committee integration
- Incident response coordination
- Audit trail expectations
- Handling public reporting obligations
- Updating policies after incidents
- Managing dual-reporting lines
- Accountability mapping technique
- Federal acquisition regulation links
- Template: governance charter
Module 6. Cross-cutting themes and patterns
Identify recurring patterns across AI domains, fairness, transparency, robustness, privacy, and learn how to apply consistent reasoning regardless of use case.
12 chapters in this module
- Fairness definition alignment
- Transparency vs. security trade-offs
- Robustness testing thresholds
- Privacy-preserving techniques
- Handling model drift detection
- Explainability for non-experts
- Supply chain transparency
- Red teaming integration
- Bias mitigation workflow
- Adversarial attack resistance
- Handling zero-day vulnerabilities
- Pattern library: common combinations
Module 7. Client communication and documentation
Produce clear, credible, and actionable reports that resonate with technical and executive audiences. Learn to structure narratives that support decision-making without overpromising.
12 chapters in this module
- Assessment summary structure
- Executive briefing technique
- Technical annex organisation
- Using visuals effectively
- Managing caveats and limitations
- Aligning tone with risk level
- Responding to reviewer comments
- Version control for artefacts
- Template: client risk memo
- Handling conflicting stakeholder views
- Language for uncertain findings
- Deliverable checklist
Module 8. Adapting the framework to federal contexts
Navigate the unique requirements of defense, intelligence, and civilian agencies. Understand how classification, mission criticality, and acquisition rules shape application.
12 chapters in this module
- Handling classified AI systems
- National security exception considerations
- Mission-critical system adaptations
- Acquisition pathway alignment
- Interfacing with DoD AI Ethical Principles
- Integrating with RMF for DoD IT
- Handling dual-use research concerns
- Supply chain risk management links
- FISMA alignment points
- Congressional reporting implications
- Working within IC guidelines
- Case study: battlefield decision support
Module 9. Integration with other standards
Connect NIST AI RMF with ISO/IEC 42001, NIST CSF 2.0, IEEE standards, and sector-specific frameworks. Build coherent multi-standard assessments.
12 chapters in this module
- ISO/IEC 42001 crosswalk
- NIST CSF 2.0 alignment
- IEEE 7000 series connections
- OCPP guidance links
- Healthcare-specific extensions
- Financial services overlays
- Transportation safety integration
- Creating unified control sets
- Avoiding duplication across audits
- Single source of truth strategy
- Mapping tools comparison
- Template: cross-standard mapping table
Module 10. Leading client framework adoption
Guide organisations through internal adoption of the framework, including training, tooling, and cultural change. Position yourself as the trusted interpreter.
12 chapters in this module
- Change management roadmap
- Internal training design
- Tool selection criteria
- Pilot programme structure
- Measuring adoption success
- Building internal champions
- Handling resistance scenarios
- Linking to performance metrics
- Sustaining momentum post-launch
- Resource allocation negotiation
- Scaling from pilot to enterprise
- Template: adoption playbook
Module 11. Proactive risk framing
Shift from reactive assessment to proactive risk shaping. Learn to anticipate emerging risks and influence design choices before deployment.
12 chapters in this module
- Influence at design phase
- Threat modelling integration
- Anticipating misuse cases
- Shaping procurement requirements
- Vendor evaluation frameworks
- Contractual risk allocation
- Design pattern recommendations
- Influence through architecture review
- Early warning indicators
- Scenario planning for future risks
- Building organisational foresight
- Template: pre-deployment risk brief
Module 12. Building your implementation playbook
Assemble a personal library of annotated examples, decision templates, and reusable logic to accelerate future work and demonstrate mastery consistently.
12 chapters in this module
- Curating your reference cases
- Documenting decision rationales
- Organising templates by use case
- Versioning your playbook
- Sharing selectively with teams
- Updating for new guidance
- Using examples in client discussions
- Annotating real project work
- Protecting proprietary insights
- Linking to internal repositories
- Maintaining artefact credibility
- Finalising your master playbook
How this maps to your situation
- Leading an AI risk assessment for a federal agency
- Supporting a client’s internal AI governance rollout
- Responding to a regulator-facing AI audit request
- Designing AI oversight mechanisms for a classified system
Before vs. after
Before
Framework application requires external references, team alignment takes longer, and client deliverables need multiple review cycles.
After
You lead framework interpretation confidently, produce client-ready outputs quickly, and anchor discussions with authoritative, repeatable logic.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3, 4 hours per module, designed to be completed over 4, 6 weeks with flexible pacing.
How this compares to the alternatives
Unlike generic AI governance overviews or vendor-specific tool trainings, this course focuses exclusively on deep command of the NIST AI RMF structure, interpretation, and application, enabling fluency that transfers across clients and contexts.
Frequently asked
Is this course focused on federal AI use cases?
Yes, the examples and adaptations are grounded in federal, defense, and regulated sector applications, aligning with common engagement types at firms like the firm.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive a certificate upon completion?
Yes, a certificate of mastery is issued after completing all modules and submitting a final framework application exercise.
$199 one-time. Approximately 3, 4 hours per module, designed to be completed over 4, 6 weeks with flexible pacing..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours