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Faster path from AI governance intent to working NIST AI RMF artefact

$199.00
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A tailored course, built for your situation

Faster path from AI governance intent to working NIST AI RMF artefact

Turn policy into practice in days, not cycles, with full NIST AI RMF traceability

$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 moves too slowly to keep up with deployment timelines

The situation this course is for

Teams design AI systems fast but stall when trying to apply governance frameworks. The gap between policy and implementation creates rework, delays, and misalignment, especially when trying to map controls to actual pipelines and documentation.

Who this is for

ICs and technical leads translating AI governance policy into working data systems, often using Microsoft and cloud integration tools

Who this is not for

Executives seeking high-level overviews, consultants without implementation experience, or professionals outside AI/data governance delivery

What you walk away with

  • Map NIST AI RMF functions directly to data pipeline components in ADF and Power BI
  • Produce traceable governance documentation aligned to NIST AI RMF in under a week
  • Re-use templates across projects to maintain consistency and reduce approval cycles
  • Anticipate auditor questions with pre-mapped control evidence from real implementations
  • Deliver working governance artefacts that integrate seamlessly with existing Databricks and Microsoft environments

The 12 modules (with all 144 chapters)

Module 1. NIST AI RMF structure and fit for data systems
Break down the NIST AI RMF into actionable components relevant to data pipeline design and business intelligence workflows.
12 chapters in this module
  1. Understanding NIST AI RMF core functions
  2. Mapping Govern to data team responsibilities
  3. Using Map to classify AI system types
  4. Assigning roles using the AI RMF playbook
  5. Integrating with existing data governance charters
  6. Version control for AI risk assessments
  7. Traceability from risk register to pipeline
  8. Naming conventions for governance artefacts
  9. Timeline for AI system documentation
  10. Cross-reference controls with Azure services
  11. Documenting model purpose and scope
  12. Linking RMF outputs to stakeholder needs
Module 2. Translating policy into data workflow design
Turn high-level AI governance statements into specific data integration patterns in ADF and Power BI.
12 chapters in this module
  1. From principle to pipeline pattern
  2. Designing ingestion with data provenance
  3. Mapping data flows to AI system boundaries
  4. Tagging sensitive data in ETL jobs
  5. Enforcing schema compliance in Power BI
  6. Automating lineage capture in ADF
  7. Embedding metadata into report layers
  8. Versioning data models with Git
  9. Documenting transformation logic
  10. Aligning refresh schedules with risk
  11. Controlling access at the source level
  12. Building audit-ready reporting layers
Module 3. Mapping controls to Azure and Microsoft stack
Connect NIST AI RMF control language to actual configurations in Azure, ADF, and Power BI environments.
12 chapters in this module
  1. Matching RMF controls to Azure permissions
  2. Using IAM roles to enforce separation
  3. Configuring ADF pipeline run logging
  4. Enabling Power BI content tracing
  5. Setting up data retention policies
  6. Applying encryption at rest and in transit
  7. Validating Databricks Unity Catalog settings
  8. Integrating Azure Monitor with alerts
  9. Linking logs to RMF reporting fields
  10. Testing access revocation workflows
  11. Auditing export functionality
  12. Verifying backup and restore procedures
Module 4. Building repeatable documentation templates
Create standardized artefacts that reduce review time and increase consistency across AI governance efforts.
12 chapters in this module
  1. Template for AI system inventory
  2. Standardized risk assessment format
  3. Data flow diagram notation
  4. Model card essentials for BI tools
  5. Version-controlled policy alignment
  6. Automated checklist generation
  7. Reusable approval workflows
  8. Cross-project evidence collection
  9. Formatting for non-technical reviewers
  10. Storing templates in shared repos
  11. Updating templates after audits
  12. Training teams on template use
Module 5. Integrating with Databricks and Unity Catalog
Adapt AI governance processes to work alongside existing data platforms without disruption.
12 chapters in this module
  1. Reading Unity Catalog schemas
  2. Mapping catalog tables to RMF data
  3. Classifying sensitive datasets
  4. Tracking table ownership updates
  5. Enforcing schema change approvals
  6. Linking model training data to catalog
  7. Using volume metadata for compliance
  8. Exporting lineage from Databricks
  9. Scheduling compliance reports
  10. Validating data quality rules
  11. Integrating with MLflow tracking
  12. Tagging assets for audit readiness
Module 6. Accelerating approval cycles with evidence packs
Assemble complete, auditor-ready submissions that reduce back-and-forth and speed up sign-off.
12 chapters in this module
  1. Components of a complete submission
  2. Pre-loading control evidence
  3. Including data flow diagrams
  4. Adding role responsibility matrices
  5. Referencing test results
  6. Packaging run logs and outputs
  7. Using hyperlinked navigation
  8. Versioning evidence bundles
  9. Labeling for internal routing
  10. Automating bundle assembly
  11. Securing package access
  12. Tracking reviewer feedback
Module 7. Versioning and iteration for AI governance
Manage changes to AI systems and governance artefacts over time without losing traceability.
12 chapters in this module
  1. Version control for risk assessments
  2. Branching for major updates
  3. Merge strategies for control changes
  4. Deploying updated documentation
  5. Tracking changes in changelogs
  6. Using pull requests for review
  7. Linking commits to tickets
  8. Auditing version history
  9. Rolling back when needed
  10. Updating downstream artefacts
  11. Communicating changes to stakeholders
  12. Archiving deprecated versions
Module 8. Cross-functional alignment techniques
Coordinate with security, legal, and product teams using shared language and artefacts.
12 chapters in this module
  1. Scheduling joint review sessions
  2. Preparing materials for legal
  3. Translating tech terms for compliance
  4. Aligning on risk thresholds
  5. Documenting escalation paths
  6. Sharing evidence across teams
  7. Using collaboration tools
  8. Setting up shared repositories
  9. Defining handoff checklists
  10. Avoiding duplication of effort
  11. Resolving conflicting requirements
  12. Building trust through consistency
Module 9. Automating governance evidence collection
Use scripts and low-code tools to gather proof of compliance without manual effort.
12 chapters in this module
  1. Scheduling ADF run log exports
  2. Automating Power BI usage reports
  3. Pulling Azure policy compliance data
  4. Generating data inventory snapshots
  5. Triggering evidence on pipeline run
  6. Using Logic Apps for notifications
  7. Building evidence dashboards
  8. Validating automation outputs
  9. Storing evidence in secure locations
  10. Alerting on missing controls
  11. Versioning generated reports
  12. Integrating with ticketing systems
Module 10. Responding to auditor requests efficiently
Reduce response time and stress during compliance reviews with pre-built workflows.
12 chapters in this module
  1. Classifying auditor questions
  2. Mapping questions to evidence
  3. Using response templates
  4. Verifying answers with owners
  5. Compiling documentation packets
  6. Meeting response deadlines
  7. Tracking outstanding items
  8. Documenting exceptions
  9. Requesting extensions when needed
  10. Updating processes post-audit
  11. Sharing findings with leadership
  12. Improving for next cycle
Module 11. Scaling governance across projects
Apply lessons from one AI system to improve speed and quality across the portfolio.
12 chapters in this module
  1. Identifying reusable components
  2. Creating governance playbooks
  3. Training new team members
  4. Standardizing naming and structure
  5. Sharing templates enterprise-wide
  6. Measuring time per project
  7. Reducing duplicate work
  8. Using feedback loops
  9. Updating standards regularly
  10. Celebrating wins across teams
  11. Documenting lessons learned
  12. Recognizing contributors
Module 12. Maintaining governance over time
Keep AI systems compliant as technology and regulations evolve.
12 chapters in this module
  1. Scheduling annual reviews
  2. Tracking regulatory changes
  3. Updating controls when needed
  4. Re-testing integrations
  5. Refreshing documentation
  6. Revalidating access controls
  7. Monitoring for drift
  8. Updating training materials
  9. Reporting on compliance health
  10. Planning for sunset phases
  11. Archiving retired systems
  12. Handing off legacy systems

How this maps to your situation

  • When starting a new AI project
  • During audit preparation
  • After a framework update
  • Before major deployment

Before vs. after

Before
Spending weeks translating AI governance policies into working systems, struggling to align with NIST AI RMF while managing data pipelines and reports.
After
Producing compliant, traceable AI governance artefacts in days , with clear mappings to ADF, Power BI, and Azure configurations.

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 to be completed alongside active projects.

If nothing changes
Without a streamlined approach, AI governance remains a bottleneck , slowing deployments, increasing rework, and creating inconsistency across projects.

How this compares to the alternatives

Unlike generic compliance courses or dense NIST commentary, this program focuses on turning governance into action , with direct mappings to the tools you use every day.

Frequently asked

Is this course technical or policy-focused?
It’s designed for technical practitioners who need to implement policy , blending NIST AI RMF structure with real-world data system configurations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work with my current tools?
Yes , it’s built around ADF, Power BI, Azure, and Databricks patterns, so you can apply it directly to your workflows.
$199 one-time. Approximately 3 hours per module , designed to be completed alongside active 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
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