A tailored course, built for your situation
Deeper command of the NIST AI RMF across high-stakes engagements
Build repeatable, auditable AI risk assessments rooted in the latest framework updates and field-tested implementation logic
$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
Mid-level consultant at a federal contractor firm delivering AI governance support across defense, intelligence, and civilian agencies; responsible for drafting risk assessments, control mappings, and compliance narratives using structured frameworks like NIST AI RMF, RMF, and SP 800-53.
Who this is not for
Executives seeking board-level summaries, vendors building AI tools, or developers focused on model tuning without governance context.
What you walk away with
- Map AI system characteristics to NIST AI RMF functions with precision and traceability
- Anticipate assessor questions and embed validation-ready evidence into initial drafts
- Produce consistent, high-quality Profile and Gap Analysis documents across engagements
- Refine risk tolerances in collaboration with client stakeholders using framework-native language
- Accelerate internal review cycles by reducing back-and-forth on framework alignment
The 12 modules (with all 144 chapters)
Module 1. Anatomy of a NIST AI RMF-compliant assessment
Break down real-world AI risk assessments to identify structural patterns, required artefacts, and common failure points in federal client deliverables.
12 chapters in this module
- Core components of a valid AI Profile
- Functions vs. categories vs. subcategories
- Linking system type to risk posture
- Defining 'harm' in mission context
- Stakeholder mapping for AI use cases
- Baseline expectations for transparency
- Risk tolerance thresholds in practice
- Version control for dynamic systems
- Audit trail requirements for updates
- Integrating human oversight points
- Documentation depth by impact level
- Common misalignments in vendor claims
Module 2. Operationalizing the Govern function
Turn policy mandates into actionable governance workflows, including documentation, escalation paths, and cross-functional coordination mechanisms.
12 chapters in this module
- Governance body design patterns
- Charter elements for AI review boards
- Decision log structure and ownership
- Escalation triggers by risk tier
- Policy integration with existing frameworks
- Roles: owner, steward, reviewer, approver
- Monitoring frequency by system class
- Compliance verification techniques
- Handling conflicting stakeholder inputs
- Tracking policy drift over time
- Updating governance artefacts post-deployment
- Reporting cadence to oversight teams
Module 3. Characterizing system risk from intake
Extract key system attributes early to determine appropriate AI RMF application depth, effort scoping, and team resourcing.
12 chapters in this module
- System classification: narrow vs. general AI
- Autonomy level and human-in-the-loop design
- Training data provenance assessment
- Model update frequency implications
- Output criticality scoring
- Use case alignment with mission risk
- Identifying dual-use concerns
- Third-party dependency mapping
- Bias potential indicators
- Safety-critical environment factors
- Deployment scale and reach
- Reversibility of system decisions
Module 4. Mapping to the Map function
Align system characteristics with specific AI RMF subcategories using documented logic, precedents, and client-specific constraints.
12 chapters in this module
- From system profile to function alignment
- Selecting appropriate risk thresholds
- Mapping to Trustworthiness outcomes
- Handling incomplete system information
- Crosswalking with NIST CSF and 800-53
- Documenting rationale for exclusions
- Using sector-specific baselines
- Addressing novel AI capabilities
- Incorporating adversarial testing results
- Weighting subcategory importance
- Versioning the mapping over time
- Client concurrence documentation
Module 5. Building a defensible Profile
Construct a clear, justified AI RMF Profile that withstands internal review and external audit by embedding evidence at every layer.
12 chapters in this module
- Baseline vs. target Profile definition
- Rationale for selecting subcategories
- Linking controls to specific harms
- Evidence requirements by subcategory
- Incorporating testing and evaluation data
- Handling legacy system exceptions
- Documenting compensating controls
- Client-specific tailoring logic
- Version control for iterative updates
- Peer review checklist integration
- Assessor expectation anticipation
- Final sign-off coordination
Module 6. Conducting Gap Analysis with precision
Identify and prioritize gaps between current state and target Profile using consistent, objective criteria that support remediation planning.
12 chapters in this module
- Gap severity scoring framework
- Current state validation techniques
- Identifying partial implementations
- Temporal vs. permanent gaps
- Resource constraints as gap context
- Third-party capability verification
- Testing coverage completeness
- Documentation gaps vs. process gaps
- Remediation effort estimation
- Stakeholder alignment on gap status
- Tracking progress toward closure
- Reporting gaps to oversight bodies
Module 7. Designing risk treatment plans
Convert gaps into executable treatment strategies with clear ownership, timelines, and success metrics aligned to AI RMF expectations.
12 chapters in this module
- Treatment options: accept, mitigate, transfer
- Control enhancement specifications
- Process change documentation
- Training and awareness integration
- Technology-based mitigation patterns
- Third-party monitoring requirements
- Acceptance criteria definition
- Ownership assignment best practices
- Timeline alignment with system lifecycle
- Budget and resource planning links
- Interdependency mapping
- Progress tracking mechanisms
Module 8. Integrating with system development lifecycle
Embed AI RMF activities into existing SDLC phases to ensure continuous compliance and reduce late-cycle rework.
12 chapters in this module
- Requirements phase integration
- Design review checkpoints
- Implementation validation points
- Testing phase alignment
- Deployment gate criteria
- Post-deployment monitoring
- Change management coordination
- Incident response linkages
- Configuration management updates
- Patch and update validation
- Decommissioning considerations
- Lifecycle documentation continuity
Module 9. Preparing for assessment and authorization
Package AI RMF artefacts for review by internal or external assessors, ensuring completeness, consistency, and defensibility.
12 chapters in this module
- Assessor expectation research
- Artefact organization standards
- Evidence package structure
- Response preparation for queries
- Interview role assignment
- Gap disclosure strategies
- Remediation plan presentation
- Compliance demonstration techniques
- Third-party assessment coordination
- Feedback incorporation process
- Reassessment readiness
- Lessons learned documentation
Module 10. Maintaining ongoing compliance
Establish processes to keep AI RMF alignment current as systems evolve, regulations shift, and new threats emerge.
12 chapters in this module
- Change detection mechanisms
- Trigger-based reassessment rules
- Continuous monitoring design
- Automated compliance checks
- Manual review frequency
- Update process for Profiles
- Stakeholder re-engagement
- Incident-driven reassessment
- Regulatory change tracking
- Internal audit coordination
- Lessons integration from incidents
- Knowledge transfer protocols
Module 11. Cross-framework alignment
Apply AI RMF in conjunction with other relevant standards and frameworks without redundancy or conflict.
12 chapters in this module
- NIST AI RMF and RMF integration
- Mapping to ISO/IEC 42001
- Alignment with EU AI Act requirements
- Crosswalking with DoD AI Ethical Principles
- Handling overlapping controls
- Consolidated evidence strategies
- Single source of truth maintenance
- Reporting harmonization
- Client-specific framework mixes
- Vendor compliance alignment
- Inter-framework conflict resolution
- Unified dashboard design
Module 12. Scaling personal expertise across teams
Replicate your mastery across project teams through templates, training, and quality assurance processes that elevate overall output.
12 chapters in this module
- Template design for reuse
- Standardizing assessment logic
- Quality review checklists
- Peer review facilitation
- Onboarding new team members
- Internal training development
- Mentorship strategies
- Feedback loop integration
- Lessons learned sharing
- Best practice documentation
- Cross-project consistency
- Expertise visibility within firm
How this maps to your situation
- Starting an AI risk assessment from scratch
- Responding to client RFP with AI governance component
- Supporting internal ATO process for AI system
- Improving consistency across multiple active engagements
Before vs. after
Before
Time spent reconciling framework requirements with client expectations, reworking drafts due to reviewer feedback, and assembling evidence post-hoc.
After
Confident, first-time-right production of AI RMF artefacts that reflect deep command of the framework and anticipate review needs.
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: 6, 8 hours over 2, 3 weeks, with modular access for just-in-time learning during active engagements.
How this compares to the alternatives
Generic NIST overviews explain the framework; this course teaches how to apply it rigorously in federal consulting environments where precision, defensibility, and repeatability determine client trust and career momentum.
Frequently asked
Is this focused on federal or commercial applications?
Designed for federal and defense-sector AI engagements, where compliance, auditability, and interagency alignment are critical.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Are there video lectures or live sessions?
No. The course is text-based with templates and examples for immediate application to active projects.
$199 one-time. 6, 8 hours over 2, 3 weeks, with modular access for just-in-time learning during active engagements..
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