A tailored course, built for your situation
Mastering NIST AI RMF for Technical Decision Influence
Build authority in AI governance decisions that shape team direction and vendor selection
$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.
Who this is for
Mid-level technical specialist in a data or AI platform company who influences tooling choices and architecture inputs without formal management authority
Who this is not for
Executives seeking board-level reporting, or practitioners focused solely on deployment engineering without governance input
What you walk away with
- Position your technical assessments as the starting point for vendor discussions
- Structure AI risk evaluations using NIST AI RMF to align cross-functional teams
- Document decision-ready outputs that get cited in architecture reviews
- Lead peer discussions on AI tools with framework-backed confidence
- Shape internal AI adoption standards before they're formalized
The 12 modules (with all 144 chapters)
Module 1. Understanding NIST AI RMF Core Structure
Break down the NIST AI Risk Management Framework into actionable layers relevant to developer workflows and integration planning.
12 chapters in this module
- Purpose of AI RMF
- Four core functions overview
- Mapping to technical roles
- AI lifecycle alignment
- Risk context definition
- Stakeholder identification
- Intended audience scope
- Relationship to other standards
- Adoption trends by sector
- Customization levels
- Organizational fit assessment
- Roadmap for implementation
Module 2. Govern Function Deep Dive
Develop governance mechanisms that assign clear ownership and accountability in AI system development and deployment.
12 chapters in this module
- Governance body formation
- Policy development process
- Risk tolerance definition
- Decision rights assignment
- Escalation paths design
- Oversight cadence planning
- Compliance tracking methods
- Stakeholder communication plan
- Ethics integration
- Performance reporting
- Audit preparation
- Framework update process
Module 3. Map Function Deep Dive
Identify and categorize AI risks in existing and proposed systems using structured taxonomies and real-world examples.
12 chapters in this module
- Hazard identification
- Use case profiling
- Context of operation analysis
- Data provenance tracking
- Model transparency assessment
- Bias detection methods
- Safety risk categorization
- Security threat modeling
- Impact level assignment
- Risk interaction mapping
- Emerging hazard monitoring
- Third-party component review
Module 4. Manage Function Deep Dive
Apply risk mitigation strategies tailored to technical implementation and operational constraints.
12 chapters in this module
- Risk treatment options
- Mitigation hierarchy
- Controls selection
- Process documentation
- Testing protocols
- Incident response design
- Monitoring setup
- Remediation workflows
- Vendor assurance process
- Change management integration
- Continuous improvement loop
- Lessons learned capture
Module 5. Applying AI RMF to Vendor Selection
Use the framework to evaluate AI and data tools before procurement, ensuring alignment with internal standards.
12 chapters in this module
- RFP integration strategy
- Scoring rubric design
- Compliance checklist creation
- Due diligence process
- Proof of concept framework
- Interoperability assessment
- Support model review
- Pricing transparency check
- Roadmap alignment analysis
- Security audit preparation
- SLA evaluation
- Exit strategy review
Module 6. Influencing Architecture Decisions
Position yourself as the go-to resource for AI system design through clear, repeatable evaluation outputs.
12 chapters in this module
- Architecture review meeting prep
- Decision record templates
- Stakeholder alignment tactics
- Trade-off documentation
- Risk communication scripts
- Alternative comparison matrix
- Cost-benefit analysis format
- Performance metrics selection
- Scalability projections
- Maintainability scoring
- Team adoption forecast
- Integration complexity index
Module 7. Building Peer-Reviewed Position Papers
Create compelling, evidence-based documents that become reference artifacts in cross-team planning.
12 chapters in this module
- Problem framing
- Stated assumptions
- Evidence collection
- Framework citation
- Alternative analysis
- Recommendation statement
- Implementation pathway
- Risk mitigation plan
- Resource estimate
- Success criteria
- Timeline projection
- Approval workflow
Module 8. AI Risk Communication for Leadership
Translate technical findings into clear, actionable briefings that inform strategic direction.
12 chapters in this module
- Executive summary writing
- Risk appetite alignment
- Business impact linkage
- Visual presentation design
- Q&A anticipation
- Tone adjustment by audience
- Urgency calibration
- Follow-up action items
- Decision tracking
- Escalation protocol
- Feedback integration
- Version control
Module 9. Integrating AI RMF into Development Workflows
Embed risk assessment steps directly into existing software development lifecycles.
12 chapters in this module
- Sprint planning integration
- Code review checklists
- Testing phase alignment
- Documentation standards
- Peer sign-off process
- Automated linting rules
- Model registry requirements
- Version tracking
- Change approval workflow
- Rollback planning
- Performance monitoring
- Post-mortem review
Module 10. Leading Cross-Functional AI Initiatives
Coordinate efforts across data, engineering, and product teams using shared risk language.
12 chapters in this module
- Initiative scoping
- Team role definition
- Shared objectives setting
- Communication rhythm
- Conflict resolution
- Progress tracking
- Dependency mapping
- Resource negotiation
- Stakeholder updates
- Milestone celebration
- Risk log maintenance
- Lessons dissemination
Module 11. Creating Repeatable AI Governance Artefacts
Develop templates and checklists that compound value across projects and reduce rework.
12 chapters in this module
- Artefact inventory
- Template design principles
- Version control setup
- Access control rules
- Usage documentation
- Review cycle planning
- Feedback loop integration
- Localization strategy
- Training materials
- Adoption tracking
- Improvement backlog
- Sunset policy
Module 12. Becoming the Internal Go-To Practitioner
Establish consistent visibility and credibility as the default advisor on AI governance matters.
12 chapters in this module
- Visibility through documentation
- Speaking up in meetings
- Volunteering for reviews
- Mentoring juniors
- Presenting findings
- Writing internal blogs
- Hosting brown bags
- Tracking influence metrics
- Building peer network
- Requesting feedback
- Refining positioning
- Maintaining relevance
How this maps to your situation
- When evaluating new AI tools
- During architecture design meetings
- Before vendor contract renewal
- After internal AI incident
Before vs. after
Before
Waiting to be included in strategic discussions about AI tooling and architecture
After
Your assessments are the starting point for peer conversations and leadership decisions
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.
How this compares to the alternatives
Unlike generic AI ethics courses, this focuses on practical application of NIST AI RMF in technical decision-making, with templates tailored to developer workflows and influence-building in peer-led environments.
Frequently asked
Who is this course designed for?
Technical professionals who want to increase their influence in AI governance and tool selection decisions without formal authority.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me lead meetings or presentations?
Yes, you'll receive templates and frameworks to lead vendor reviews and architecture discussions confidently.
$199 one-time. Approximately 3 hours per week over 4 weeks to complete all modules and apply templates..
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