A tailored course, built for your situation
Mastering NIST AI RMF for Machine Learning Compliance Practitioners
Build auditable, regulator-ready AI governance decisions grounded in NIST AI RMF, without slowing innovation.
$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.
AI governance is no longer theoretical, it’s in the audit trail, the merger packet, and the regulator’s inquiry.
The situation this course is for
Practitioners are being asked to justify AI systems they didn’t design, using frameworks they weren’t trained on. Without structured grounding, teams default to over-documentation, deferral, or reactive fixes, all of which erode trust.
Who this is for
Mid-to-senior data or ML engineers with compliance-adjacent responsibilities, particularly in regulated or pre-IPO environments where AI governance rigor is rising.
Who this is not for
Entry-level data analysts, general IT staff, or leadership seeking board-level summaries. This is for practitioners who draft, review, or sign off on technical governance artefacts.
What you walk away with
- Produce regulator-ready AI risk assessments using NIST AI RMF structure
- Own escalation triage from peer teams with documented decision criteria
- Deliver M&A diligence inputs that survive senior review
- Build repeatable templates for model governance documentation
- Gain recognition as the internal reference for AI compliance decisions
The 12 modules (with all 144 chapters)
Module 1. Foundations of NIST AI RMF
Understand the four core functions of NIST AI RMF , Map, Measure, Manage, Govern , and how they apply to real-world ML systems. Learn to identify where your current work fits and where gaps exist in documentation or ownership.
12 chapters in this module
- Core principles of AI risk management
- Structure of the NIST AI RMF framework
- Map function: Identifying AI system boundaries
- Measure function: Quantifying risk exposure
- Manage function: Mitigation strategies
- Govern function: Oversight mechanisms
- Mapping to internal AI inventories
- Integration with model lifecycle
- Linking to data provenance
- Role clarity in AI governance
- Documentation standards for auditors
- Pre-audit preparation workflow
Module 2. AI System Scoping and Inventory
Define what constitutes an AI system under NIST RMF, focusing on classification, autonomy, and impact. Build a living inventory that supports both internal tracking and external reporting.
12 chapters in this module
- Defining AI vs automation
- Thresholds for AI classification
- Autonomy levels in model behavior
- Impact scoring criteria
- Ownership assignment protocols
- Version tracking for models
- Data lineage integration
- External dependency mapping
- Third-party model inclusion
- Legacy system classification
- Inventory update cadence
- Audit-ready export formats
Module 3. Risk Mapping Across ML Lifecycles
Apply NIST AI RMF to each stage of model development , from ideation to deprecation. Identify risk touchpoints and document mitigation actions.
12 chapters in this module
- Risk at model conception
- Data acquisition red flags
- Feature engineering risks
- Training environment controls
- Bias detection timing
- Validation thresholds
- Deployment approval criteria
- Monitoring drift sensitivity
- Incident response roles
- Model retraining triggers
- Sunset and archiving steps
- Documentation retention rules
Module 4. Governance Artefact Development
Create standardized, reusable documents that meet internal and external scrutiny , including AI impact assessments, model cards, and compliance checklists.
12 chapters in this module
- AI impact assessment structure
- Model card components
- Compliance checklist design
- Stakeholder sign-off fields
- Version control integration
- Template reuse patterns
- Audit trail alignment
- Regulator-facing summaries
- Escalation handling logs
- Peer review workflows
- Change approval paths
- Cross-team distribution protocols
Module 5. M&A and Due Diligence Preparation
Prepare AI governance artefacts for merger and acquisition scrutiny. Anticipate buyer questions and structure responses that reduce liability exposure.
12 chapters in this module
- AI assets in due diligence
- Model ownership clarity
- Licensing compliance checks
- Bias audit readiness
- Third-party dependency review
- Data consent verification
- Model performance guarantees
- Documentation completeness score
- Escalation history review
- Remediation plan templates
- Liability transfer protocols
- Pre-acquisition audit simulation
Module 6. Regulator-Facing Documentation
Structure outputs that satisfy regulator inquiries , from initial requests to formal responses. Use NIST AI RMF to demonstrate systematic oversight.
12 chapters in this module
- Regulator inquiry triage
- Response ownership assignment
- Evidence package assembly
- Timeline adherence strategies
- Internal escalation paths
- Legal team coordination
- Draft review process
- Final version control
- Submission tracking
- Follow-up preparation
- Post-response audit trail
- Pattern recognition across inquiries
Module 7. Cross-Team Escalation Handling
Establish protocols for receiving, triaging, and resolving AI governance issues raised by peer teams , from data science to legal.
12 chapters in this module
- Escalation intake process
- Triage criteria by severity
- Ownership determination rules
- Response SLAs
- Documentation requirements
- Stakeholder notification
- Root cause analysis
- Remediation tracking
- Peer feedback integration
- Trend reporting
- Process improvement cycles
- Knowledge base contributions
Module 8. Internal Audit Readiness
Align AI governance work with internal audit expectations. Build a standing posture that passes review without last-minute fixes.
12 chapters in this module
- Audit scope anticipation
- Control mapping to NIST RMF
- Evidence location index
- Interview preparation
- Gap response playbook
- Corrective action tracking
- Follow-up audit planning
- Process maturity scoring
- Self-assessment tools
- Audit communication protocol
- Post-audit review process
- Improvement backlog management
Module 9. Model Risk Governance Integration
Integrate NIST AI RMF with existing model risk management frameworks , ensuring consistency across compliance domains.
12 chapters in this module
- Model risk policy alignment
- Governance committee structure
- Independent validation steps
- Risk tier classification
- Approval authority levels
- Documentation standards
- Change management process
- Stress testing integration
- Loss estimation methods
- Model inventory alignment
- Regulatory reporting sync
- Executive summary templates
Module 10. Policy to Implementation Workflow
Turn high-level AI governance policies into working systems , with clear ownership, timelines, and validation steps.
12 chapters in this module
- Policy decomposition steps
- Actionable control derivation
- Owner assignment matrix
- Implementation timelines
- Tooling integration
- Validation checklists
- Pilot testing design
- Feedback collection
- Rollout sequencing
- Training delivery
- Monitoring setup
- Post-launch review
Module 11. Stakeholder Communication Strategies
Communicate AI governance decisions effectively to technical and non-technical audiences , from engineers to executives.
12 chapters in this module
- Technical summary writing
- Executive briefing structure
- Risk translation framework
- Presentation slide design
- Q&A preparation
- Escalation narrative crafting
- Regulator communication tone
- Legal team alignment
- Public relations coordination
- Internal comms planning
- Feedback loop creation
- Message consistency checks
Module 12. Sustaining AI Governance Maturity
Build systems that maintain compliance over time , even with team changes, new regulations, or shifting business priorities.
12 chapters in this module
- Governance maturity model
- Progress tracking metrics
- Team onboarding process
- Leadership transition planning
- Regulation change monitoring
- Framework update protocol
- Lessons learned capture
- Benchmarking participation
- Tooling upgrade planning
- External audit prep cycle
- Continuous improvement rhythm
- Knowledge transfer mechanisms
How this maps to your situation
- Pre-M&A due diligence phase
- Regulator inquiry response
- Peer team escalation handling
- Internal audit preparation
Before vs. after
Before
Reactive handling of AI governance requests, inconsistent documentation, and deferred ownership across teams.
After
Proactive ownership of high-impact AI governance work , M&A inputs, regulator reviews, peer escalations , with standardized, auditable outputs.
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 hours per week over 4 weeks to complete all modules and apply templates to current work.
If nothing changes
Without structured grounding in NIST AI RMF, practitioners risk being bypassed when critical AI governance decisions are made , even as their technical work underpins them.
How this compares to the alternatives
Generic AI governance trainings cover broad principles. This course delivers actionable, role-specific frameworks tied directly to NIST AI RMF , with artefacts designed for real-world use in M&A, audits, and peer escalations.
Frequently asked
Is this course technical or compliance-focused?
It’s designed for technical practitioners operating in compliance-adjacent roles , like ML engineers who produce governance artefacts for audit or review.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Do I need prior NIST experience?
No. The course starts with foundations and builds to advanced application.
$199 one-time. Approximately 3 hours per week over 4 weeks to complete all modules and apply templates to current work..
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