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AIG7979 Mastering NIST AI RMF for Senior Data and AI Governance Practitioners

$199.00
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A tailored course, built for your situation

Mastering NIST AI RMF for Senior Data and AI Governance Practitioners

A structured path to influence critical AI decisions through authoritative, implementation-ready control design

$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.
Vendor selection briefs requiring last-minute traceability adjustments

The situation this course is for

AI governance inputs are often retrofitted to procurement and architecture review cycles, creating rework and diluting technical authority. Teams that preempt this cycle own the narrative.

Who this is for

Senior IC-level practitioner in data or AI governance at a high-growth enterprise tech company, responsible for shaping policy implementation and cross-functional alignment on technical risk

Who this is not for

Junior analysts, pure-play engineers without governance scope, or executives seeking only overview summaries

What you walk away with

  • Produce AI governance documentation that aligns directly with NIST AI RMF core functions and is accepted in first review
  • Design traceable control mappings from framework requirements to implemented data and model workflows
  • Shape vendor selection criteria with formally referenced risk thresholds
  • Lead technical review sessions with confidence, using source-backed justifications for control design
  • Build repeatable templates that reduce cycle time from framework update to implementation guidance

The 12 modules (with all 144 chapters)

Module 1. Understanding NIST AI RMF Core Functions and Intent
Foundational clarity on the framework’s structure, goals, and alignment with other standards. Focuses on actionable interpretation of each function (Govern, Map, Measure, Govern) for technical implementation.
12 chapters in this module
  1. Defining the NIST AI RMF scope without overreach
  2. Distinguishing between governance and technical controls
  3. Mapping framework objectives to data platform guardrails
  4. How the AI RMF interacts with SOC 2 and ISO 27001
  5. Understanding the role of risk tolerance in AI systems
  6. Key differences from traditional IT risk frameworks
  7. Identifying where AI risk diverges from data risk
  8. Framework structure: Categories, subcategories, outcomes
  9. Interpreting 'responsible AI' in operational terms
  10. Alignment with OECD AI Principles and EU AI Act
  11. Using the AI RMF Playbook for scenario planning
  12. Integration points with MLOps and data lifecycle
Module 2. Embedding Governance Through Organizational Roles
Clarifies how teams define ownership, escalation paths, and decision rights. Ensures governance is operationalized, not just documented.
12 chapters in this module
  1. Defining accountability for AI risk ownership
  2. Establishing cross-functional governance committees
  3. Documenting delegation of authority for AI decisions
  4. Creating escalation paths for high-impact models
  5. Role taxonomy for AI governance stakeholders
  6. Integrating governance roles into sprint planning
  7. Maintaining separation of duties in practice
  8. How data stewards interact with AI oversight
  9. Vendor governance integration in procurement
  10. Training engineers on governance responsibilities
  11. Updating role definitions during organizational shifts
  12. Auditing governance role assignments
Module 3. Mapping AI System Boundaries and Data Flows
Teaches how to define system scope clearly for risk assessment. Focuses on data provenance, model dependencies, and lifecycle phases.
12 chapters in this module
  1. Identifying all components in an AI pipeline
  2. Documenting training data origin and lineage
  3. Tracing inference inputs to decision points
  4. Classifying model types by risk exposure
  5. Defining system lifecycle stages for governance
  6. Data drift monitoring thresholds and triggers
  7. Third-party data and model dependencies
  8. Model versioning and retraining workflow
  9. Integration with Unity Catalog for metadata
  10. Generating audit-ready system diagrams
  11. Handling edge cases in data pipelines
  12. Maintaining boundary documentation over time
Module 4. Assessing Risk Across the AI Lifecycle
Provides a method for evaluating risk at each stage , from design to decommissioning. Emphasizes measurable criteria over subjective judgment.
12 chapters in this module
  1. Defining risk criteria for AI use cases
  2. Using likelihood and impact scales consistently
  3. Scoring model interpretability requirements
  4. Evaluating fairness across demographic groups
  5. Assessing safety and reliability in real-world use
  6. Identifying security vulnerabilities in models
  7. Third-party model risk evaluation process
  8. Documenting risk tolerance decisions
  9. Reassessing risk at model retraining
  10. Integrating risk scores into CI/CD pipelines
  11. Generating risk heatmaps for leadership
  12. Maintaining risk assessment version history
Module 5. Designing Effective Risk Controls
Teaches how to build controls that are testable, measurable, and integrated into workflows. Avoids generic checklists.
12 chapters in this module
  1. Writing specific, auditable control statements
  2. Linking controls directly to risk outcomes
  3. Designing pre-deployment validation gates
  4. Automating control checks in MLOps pipelines
  5. Creating human-in-the-loop review processes
  6. Documenting control ownership and thresholds
  7. Integrating model monitoring with control logic
  8. Ensuring controls adapt with model updates
  9. Using templates for consistent control design
  10. Aligning controls with data quality standards
  11. Testing control effectiveness with red teams
  12. Updating controls based on incident feedback
Module 6. Implementing Controls in Data and Model Systems
Covers technical integration of controls into existing platforms. Focuses on traceability, automation, and sustainability.
12 chapters in this module
  1. Integrating controls into Delta Lake workflows
  2. Using Unity Catalog for policy enforcement
  3. Tagging models and data with compliance metadata
  4. Automating data drift detection and alerts
  5. Enforcing model validation gates in CI/CD
  6. Logging control decisions for auditability
  7. Securing model access with role-based controls
  8. Testing control resilience under failure
  9. Monitoring control performance over time
  10. Versioning control configurations
  11. Documenting control implementation details
  12. Scaling controls across teams and projects
Module 7. Monitoring and Measuring Control Performance
Establishes metrics and feedback loops to ensure controls remain effective. Focuses on continuous improvement.
12 chapters in this module
  1. Defining KPIs for control effectiveness
  2. Tracking false positive rates in monitoring
  3. Measuring time to detect and respond to drift
  4. Auditing control logs for completeness
  5. Generating monthly control performance reports
  6. Benchmarking against peer teams
  7. Using dashboards to visualize control health
  8. Identifying control decay over time
  9. Incorporating feedback from incident reviews
  10. Adjusting thresholds based on operational data
  11. Reporting metrics to governance committees
  12. Archiving performance data for audits
Module 8. Preparing for Internal and External Reviews
Teaches how to package evidence for audits, vendor reviews, and leadership scrutiny. Focuses on clarity and completeness.
12 chapters in this module
  1. Organizing documentation by NIST AI RMF function
  2. Creating evidence maps for each control
  3. Preparing for SOC 2 review with AI focus
  4. Responding to auditor follow-up questions
  5. Documenting exceptions and compensating controls
  6. Maintaining version-controlled review packages
  7. Generating vendor-facing compliance summaries
  8. Training team members on evidence requests
  9. Streamlining internal review workflows
  10. Using templates to reduce rework
  11. Automating evidence collection from tools
  12. Validating package completeness pre-submission
Module 9. Integrating AI RMF Into Vendor Selection
Shows how to use the framework to shape procurement and third-party risk assessments.
12 chapters in this module
  1. Including AI risk criteria in RFPs
  2. Evaluating vendor AI governance maturity
  3. Requiring NIST AI RMF alignment from vendors
  4. Assessing third-party model explainability
  5. Reviewing vendor data practices and policies
  6. Documenting vendor risk acceptance
  7. Creating vendor audit right clauses
  8. Tracking vendor compliance over time
  9. Integrating vendor data into internal risk dashboards
  10. Handling vendor incidents and escalations
  11. Managing contract renewals with risk review
  12. Building preferred vendor lists based on controls
Module 10. Leading Cross-Functional AI Governance Initiatives
Equips practitioners to lead without authority. Focuses on influence, documentation, and stakeholder alignment.
12 chapters in this module
  1. Building credibility through consistent output
  2. Communicating risk in business terms
  3. Running effective governance meetings
  4. Gaining buy-in from engineering leads
  5. Aligning AI risk posture with business goals
  6. Resolving conflicts between teams
  7. Creating shared ownership of controls
  8. Using data to support governance positions
  9. Maintaining momentum during leadership changes
  10. Scaling governance across business units
  11. Recognizing team contributions publicly
  12. Measuring influence through adoption
Module 11. Maintaining Governance Documentation Over Time
Covers update cycles, version control, and knowledge transfer. Ensures sustainability.
12 chapters in this module
  1. Scheduling regular framework reviews
  2. Tracking changes in NIST guidance
  3. Updating control mappings after platform changes
  4. Versioning governance documents
  5. Archiving obsolete policies and controls
  6. Training new team members on existing controls
  7. Automating documentation refreshes
  8. Linking documentation to incident post-mortems
  9. Reviewing documentation with legal and compliance
  10. Ensuring accessibility across teams
  11. Maintaining multilingual versions if needed
  12. Auditing documentation completeness annually
Module 12. Scaling Governance Across Teams and Platforms
Teaches how to extend governance consistently across an organization. Focuses on enablement, not enforcement.
12 chapters in this module
  1. Designing governance enablement programs
  2. Creating self-service policy toolkits
  3. Training platform teams on AI risk basics
  4. Integrating governance into onboarding
  5. Using internal newsletters to share best practices
  6. Building communities of practice
  7. Recognizing and rewarding governance champions
  8. Scaling automation to reduce burden
  9. Adapting frameworks for different risk profiles
  10. Managing exceptions with transparency
  11. Evaluating governance maturity across teams
  12. Reporting enterprise-wide posture to leadership

How this maps to your situation

  • Initial framework adoption
  • Control implementation in production
  • Audit and review preparation
  • Scaling governance across teams

Before vs. after

Before
AI governance inputs are reactive, retrofitted, and inconsistent across teams.
After
Governance is proactive, referenced, and integrated into technical and procurement 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: 90 minutes per week for 6 weeks, or self-paced over 12 weeks.

If nothing changes
Without structured governance, AI initiatives face delayed approvals, rework during audits, and diminished influence on platform direction.

How this compares to the alternatives

Generic AI ethics courses lack technical depth. Internal training is inconsistent. This course delivers structured, NIST-aligned control design with direct implementation pathways.

Frequently asked

Is this course technical or strategic?
It's both: grounded in technical implementation but structured around NIST's governance framework to support strategic influence.
Can I apply this to non-NIST frameworks?
Yes , the structure maps to OECD, EU AI Act, and ISO 42001, though NIST is the primary anchor.
$199 one-time. 90 minutes per week for 6 weeks, or self-paced over 12 weeks..

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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