A tailored course, built for your situation
Faster path from AI policy intent to working NIST AI RMF artefact
A 12-module build-your-own playbook for deploying the NIST AI RMF with precision and velocity
$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.
Stalled AI governance rollouts despite clear policy direction
The situation this course is for
Teams draft robust AI governance policies but struggle to translate them into working artefacts, risk matrices, control mappings, compliance checks, without delays, rework, or cross-team bottlenecks. The NIST AI RMF provides structure, but the path from intent to implementation remains unclear, slowing deployment and weakening trust.
Who this is for
Senior data or AI governance practitioner in a technical leadership role, responsible for turning frameworks into working systems. Works across engineering, compliance, and product. Values precision, speed, and peer credibility. Already familiar with NIST AI RMF or similar frameworks.
Who this is not for
Individuals seeking introductory AI ethics content or general compliance overviews. Not for those without influence over AI system design or governance rollouts. Not for teams still debating whether to adopt a framework.
What you walk away with
- Produce NIST AI RMF-compliant risk assessment reports in under 10 days
- Map governance controls to technical implementation with traceable artefacts
- Deploy a working AI governance playbook that integrates with engineering workflows
- Respond to internal audits with pre-built templates and evidence trails
- Lead cross-functional AI governance sprints without relying on external consultants
The 12 modules (with all 144 chapters)
Module 1. Aligning AI governance to engineering velocity
Bridge the gap between compliance frameworks and fast-moving data and AI teams. Learn how to translate NIST AI RMF principles into engineering milestones without slowing innovation.
12 chapters in this module
- Governance as enabler not gatekeeper
- Matching AI risk tiers to sprint cycles
- Working definitions of AI system boundary
- Embedding governance into CI/CD pipelines
- Speed-to-review tradeoffs by use case
- Documenting intent without over-specifying
- Versioning policy alongside model updates
- Tracking drift between policy and practice
- Engineering feedback loops into governance
- Measuring governance throughput
- Defining 'done' for governance tasks
- From abstract principle to code comment
Module 2. Rapid risk profiling using NIST AI RMF Core
Accelerate initial risk assessment using the NIST AI RMF Core functions. Build repeatable templates for categorizing AI systems by impact, data provenance, and decision autonomy.
12 chapters in this module
- Core Function 1 rapid triage
- Core Function 2 fast mapping
- Core Function 3 threshold rules
- Core Function 4 sprint alignment
- Core Function 5 review triggers
- High-impact use case filters
- Low-risk exemptions by design
- Scoring model interpretability tiers
- Automated data lineage checks
- Bias detection entry points
- Security exposure by model type
- Human oversight thresholds
Module 3. From framework function to control blueprint
Turn each NIST AI RMF function into a technical control set. Create implementation-ready blueprints that engineers can action without ambiguity.
12 chapters in this module
- Mapping Map to model registry
- Controlling risk thresholds
- Baseline for monitoring dashboards
- Governance SLAs by risk tier
- API contract guardrails
- Data drift detection frequency
- Model update approval paths
- Human-in-the-loop triggers
- Red team access protocols
- Incident response playbooks
- Fallback mechanism specs
- Decommissioning checklists
Module 4. Template-driven documentation sprints
Run time-boxed sessions to generate complete, audit-ready documentation. Use structured templates to eliminate rework and ensure consistency across teams.
12 chapters in this module
- 90-minute risk assessment format
- One-page model overview template
- AI system boundary canvas
- Stakeholder alignment matrix
- Compliance evidence checklist
- Audit trail requirements
- Version history format
- Change impact summary
- Risk acceptance form
- Third-party vendor checklist
- Model card integration
- Documentation automation tools
Module 5. Cross-functional review coordination
Orchestrate fast, effective reviews with legal, security, and product teams. Design lightweight processes that provide coverage without delay.
12 chapters in this module
- Pre-read package structure
- Stakeholder-specific summaries
- Legal review triggers
- Security sign-off criteria
- Product team feedback window
- Escalation paths for disagreement
- Review calendar sync
- Decision logging format
- Action item tracking
- Follow-up cadence
- Remote review best practices
- Review completion definition
Module 6. Evidence packaging for internal audit
Assemble complete, coherent evidence packages on demand. Ensure every control has a matching artefact, making audits faster and less disruptive.
12 chapters in this module
- Audit-ready evidence checklist
- Control-to-artefact mapping table
- Model documentation bundle
- Training data provenance log
- Bias testing report format
- Security logging standards
- Access control snapshots
- Incident response records
- Change approval trail
- Governance committee minutes
- Compliance exception log
- Automated evidence collection
Module 7. Automated governance workflows
Integrate governance checks into existing pipelines. Reduce manual effort and increase consistency by automating risk assessments, documentation, and compliance checks.
12 chapters in this module
- Triggering governance on model commit
- Auto-generating model cards
- Data lineage capture hooks
- Risk score calculation engine
- Policy version checking
- Automated control validation
- Documentation template fills
- Alerting on threshold breach
- Integration with Jira tickets
- Slack notification rules
- Dashboard widgets for oversight
- APIs for cross-tool sync
Module 8. Staged rollout by AI risk tier
Deploy governance in phases based on system impact. Focus effort where it matters most, avoiding blanket processes that slow innovation.
12 chapters in this module
- Risk tier definition matrix
- High-risk system onboarding
- Medium-risk automation level
- Low-risk self-certification
- Exempt category rules
- Use case classification guide
- Review depth by tier
- Documentation burden scaling
- Audit frequency by tier
- Governance SLA definitions
- Escalation triggers
- Tier re-evaluation schedule
Module 9. Vendor AI oversight integration
Extend governance to third-party models and platforms. Ensure external AI components meet your risk and compliance standards.
12 chapters in this module
- Vendor risk assessment template
- Third-party model audit rights
- API security requirements
- Data handling commitments
- Performance transparency
- Model update notification
- Fallback mechanism assurance
- Vendor documentation standards
- Contractual leverage points
- Penalty clauses for non-compliance
- Multi-vendor comparison matrix
- Exit strategy planning
Module 10. Living compliance artefacts
Keep governance outputs current as systems evolve. Move from static documents to dynamic, versioned artefacts that reflect real-world changes.
12 chapters in this module
- Versioning model documentation
- Change impact notifications
- Automated update triggers
- Living model cards
- Dynamic risk register
- Drift detection alerts
- Review cycle automation
- Stakeholder re-approval paths
- Historical comparison tools
- Archival and retrieval
- Decommissioning documentation
- Audit trail retention
Module 11. Metrics that measure governance health
Track what matters: speed, coverage, and rework. Use data to refine your approach and demonstrate value to leadership.
12 chapters in this module
- Cycle time from draft to sign-off
- Rework rate by domain
- Coverage gap tracking
- Audit finding recurrence
- Stakeholder satisfaction
- Control effectiveness rate
- Documentation completeness
- Review cycle duration
- Risk detection lead time
- Compliance exception volume
- Governance debt index
- Team throughput benchmark
Module 12. Scaling governance without bloat
Grow governance capacity with demand, not headcount. Use templates, automation, and tiering to maintain speed at scale.
12 chapters in this module
- Template reuse across teams
- Centralised playbook access
- Peer review networks
- Automated guidance bots
- Knowledge base maintenance
- Onboarding accelerators
- Self-service documentation
- Governance champion program
- Cross-team alignment rituals
- Standardisation vs flexibility
- Feedback loop integration
- Continuous improvement cycle
How this maps to your situation
- When spinning up a new AI system
- Before internal audit cycles
- During vendor selection for AI tools
- After model updates or retraining
Before vs. after
Before
AI governance efforts stall between policy and implementation, with inconsistent artefacts, delayed reviews, and audit rework.
After
You ship complete, compliant, and engineer-aligned AI governance outputs in weeks, with reusable templates and automated workflows.
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 module, designed for professionals to apply concepts directly to current projects.
If nothing changes
Continuing with ad-hoc or manual AI governance processes leads to slower deployment, higher rework costs, and increased audit findings, eroding trust in your team's ability to scale responsibly.
How this compares to the alternatives
Unlike generic AI ethics courses or broad compliance certifications, this program focuses exclusively on accelerating NIST AI RMF implementation with working artefacts, not theory. No other offering combines framework mastery with execution speed at this level of technical detail.
Frequently asked
Is this course technical or policy-focused?
It bridges both. You’ll learn how to translate NIST AI RMF policy intent into technical implementation and documentation that engineers and auditors accept.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I get practical templates?
Yes. Each module includes a downloadable template or worked example you can apply immediately to your work.
$199 one-time. Approximately 3 hours per module, designed for professionals to apply concepts directly to current projects..
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