A tailored course, built for your situation
Mastering NIST AI RMF for Data Platform Governance Practitioners
Turn AI governance frameworks into cross-functional leverage without overextending your team
$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 work stays siloed and reactive
The situation this course is for
Even skilled practitioners find their influence capped when speaking across security, compliance, and engineering, especially without a shared, authoritative framework to align to. Without structured methods, AI governance becomes reactive, inconsistent, and confined to pockets of excellence.
Who this is for
Senior IC in data/AI governance at a cloud-scale tech company, operating at the intersection of platform, policy, and delivery
Who this is not for
Entry-level analysts, individual contributors focused only on code deployment, or executives seeking board-level summaries
What you walk away with
- Deploy NIST AI RMF in modular fashion tailored to specific AI use cases across business units
- Map technical controls to governance outcomes so engineering and compliance speak the same language
- Lead cross-functional AI risk assessments using a recognized standard
- Package governance decisions into reusable templates that scale across regions
- Anticipate auditor and regulator questions using documented alignment to NIST AI RMF functions
The 12 modules (with all 144 chapters)
Module 1. Understanding the NIST AI RMF Core Structure
Break down the four core functions, Govern, Map, Measure, and Manage, and how they apply to real-world AI deployments across cloud data platforms.
12 chapters in this module
- What the NIST AI RMF is (and isn’t)
- Govern function: Policy design at scale
- Map function: Linking AI components to risk domains
- Measure function: Quantifying AI behavior fairly
- Manage function: Operationalizing oversight
- How it differs from ISO 42001 and AI Act
- When to use it standalone vs. with SOC 2
- Mapping to internal AI review boards
- Key artifacts produced at each stage
- Common misapplications to avoid
- How Databricks teams use similar logic
- First step: scoping an AI system
Module 2. Scoping AI Systems with Precision
Define AI system boundaries clearly so governance applies only where needed, avoiding blanket restrictions.
12 chapters in this module
- What constitutes an AI system under NIST
- Identifying model lifecycle phases
- Delineating data pipelines from inference layers
- Boundary decisions for MLOps platforms
- Human-in-the-loop thresholds
- Real-time vs. batch scoring distinctions
- When a feature store becomes part of the system
- Documenting scope for audit readiness
- Avoiding overreach in scoping
- Case: scoping a recommendation engine
- Case: scoping a fraud detection model
- Template: AI system boundary statement
Module 3. Govern Function: Establishing Oversight Structures
Design lightweight governance bodies that make real decisions without bureaucracy.
12 chapters in this module
- AI governance vs. data governance scope
- Who should sit on an AI review board
- Decision rights for model approval
- Delegation patterns for high-velocity teams
- Escalation paths for contested models
- Integration with existing risk committees
- Role of platform engineers in governance
- Documenting governance charters
- Cadence of review cycles
- Case: AI governance at a fintech client
- Avoiding governance theater
- Template: AI Oversight Charter
Module 4. Map Function: Linking Components to Risk Domains
Connect technical architecture to compliance and ethical risk using structured mapping techniques.
12 chapters in this module
- Four data risk dimensions under NIST
- Model transparency requirements
- Mapping training data lineage
- Third-party model dependencies
- Identifying safety-critical outputs
- Bias assessment entry points
- Security boundaries in inference APIs
- Mapping to SOC 2 trust services
- Case: customer churn prediction model
- Case: document classification system
- Common mapping oversights
- Template: Component-to-Risk Domain Matrix
Module 5. Measure Function: Evaluating AI Performance Fairly
Go beyond accuracy metrics to assess fairness, safety, and robustness across diverse use cases.
12 chapters in this module
- Accuracy vs. reliability distinctions
- Fairness metrics by use case
- Representational harm identification
- Adversarial robustness testing
- Model drift monitoring triggers
- Human feedback integration
- When to require red-team testing
- Case: creditworthiness model fairness
- Case: healthcare triage AI
- Threshold setting for escalation
- Documenting measurement rationale
- Template: AI Measurement Scorecard
Module 6. Manage Function: Operationalizing AI Oversight
Turn governance decisions into ongoing monitoring and improvement practices.
12 chapters in this module
- Incident response for AI failures
- Model versioning and rollback plans
- Monitoring for unintended consequences
- User feedback loops
- Updating models under regulatory scrutiny
- Decommissioning outdated AI systems
- Audit trail requirements
- Case: handling a biased model in production
- Case: responding to regulator inquiry
- Template: AI Incident Response Playbook
- Template: Model Retraining Checklist
- Handover protocols for offboarding
Module 7. Integrating NIST AI RMF with Existing Compliance Programs
Align AI governance with SOC 2, ISO 27001, and privacy programs without duplicating effort.
12 chapters in this module
- SOC 2 trust services overlap
- Mapping NIST functions to ISO 27001 controls
- GDPR and AI Act synergies
- Privacy threshold assessments
- Integrating with SecOps workflows
- Leveraging Unity Catalog metadata
- Delta Lake audit logging use cases
- Avoiding compliance redundancy
- Case: multi-region AI compliance
- Template: Cross-framework Alignment Table
- Case: joint SOC 2 and AI RMF audit
- Template: Unified Control Mapping
Module 8. Cross-Team Communication Using NIST AI RMF
Use the framework to create shared understanding between engineering, legal, and product teams.
12 chapters in this module
- Translating risk into engineering tasks
- Legal team collaboration points
- Product manager engagement tactics
- Simplifying RMF for non-experts
- Workshop design for cross-functional teams
- Managing conflicting priorities
- Using RMF to de-escalate disputes
- Case: launching an AI feature in EU
- Case: prioritizing model updates
- Template: Stakeholder Communication Plan
- Template: AI Readiness Dashboard
- Facilitation guide for RMF workshops
Module 9. Scaling Governance Across Regions and Use Cases
Replicate governance outcomes across geographies and business units without rework.
12 chapters in this module
- Regional adaptation strategy
- EU vs. US vs. APAC enforcement trends
- Localization of fairness criteria
- Centralized vs. federated models
- Playbook reuse across teams
- Version control for governance templates
- Training regional champions
- Case: global fraud detection rollout
- Case: HR AI tool localization
- Template: Regional Governance Adoption Plan
- Template: Cross-Region Alignment Tracker
- Audit consistency checklist
Module 10. Documentation and Audit-Ready Artifact Creation
Produce clear, defensible records that satisfy internal and external reviewers.
12 chapters in this module
- Essential documentation types
- AI risk register design
- Model cards vs. SoA documents
- Evidence collection strategy
- Preparing for AI audits
- Common auditor questions
- How much detail is enough
- Case: passing a surprise audit
- Template: AI Governance Portfolio
- Template: Model Attestation Form
- Versioning governance artifacts
- Retention policies for AI records
Module 11. Implementing Playbooks Across the AI Lifecycle
Embed governance into CI/CD, MLOps, and platform workflows.
12 chapters in this module
- Gate review design for CI/CD
- Automated compliance checks
- Model registration requirements
- Pre-deployment checklist integration
- Post-deployment monitoring rules
- Feedback loop design
- Case: integrating with MLflow
- Case: platform-level enforcement
- Balancing speed and control
- Template: Pre-Deployment Gate Template
- Template: Model Registration Form
- Playbook: AI Launch Sequence
Module 12. Sustaining Governance Through Team and Leadership Change
Ensure institutional knowledge survives turnover and strategic shifts.
12 chapters in this module
- Knowledge transfer protocols
- Succession planning for AI roles
- Documenting unwritten rules
- Onboarding new team members
- Leadership transition comms
- Updating playbooks quarterly
- Measuring governance maturity
- Case: surviving an org restructuring
- Case: onboarding new CISO
- Template: Governance Maturity Assessment
- Template: Knowledge Transfer Checklist
- Playbook: Governance Health Review
How this maps to your situation
- Implementing AI governance in a multi-region tech org
- Leading AI risk assessments across product teams
- Scaling compliance practices across data platforms
- Establishing authority in cross-functional AI decisions
Before vs. after
Before
AI governance feels reactive, siloed, and inconsistent across teams and regions.
After
You lead with a structured, recognized framework that extends your influence across functions and geographies.
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, or 36 hours total, designed to be consumed incrementally alongside day-to-day work.
If nothing changes
Without a clear governance framework, AI initiatives risk inconsistency, audit findings, or ethics controversies that could slow innovation across the organization.
How this compares to the alternatives
Unlike generic AI ethics courses or academic overviews, this course delivers structured, actionable methods tied to a recognized standard, so you can implement it immediately in your environment.
Frequently asked
Is this course about Databricks or Unity Catalog?
No. The course focuses on the NIST AI RMF framework and how to apply it in multi-team, multi-region environments, without centering on any single vendor or platform.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me influence teams outside my own?
Yes. The course teaches how to use the NIST AI RMF to build shared understanding across engineering, compliance, product, and regional teams, extending your reach intentionally.
$199 one-time. Approximately 3 hours per module, or 36 hours total, designed to be consumed incrementally alongside day-to-day 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