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Deeper Command of the NIST AI Risk Management Framework

$199.00
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A tailored course, built for your situation

Deeper Command of the NIST AI Risk Management Framework

Master the structure, decisions, and artefacts that define modern AI governance at scale

$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

Senior governance leader operating at the intersection of AI policy and enterprise delivery

Who this is not for

Junior analysts, entry-level compliance staff, or practitioners focused only on technical AI implementation without governance oversight

What you walk away with

  • Internal fluency in the NIST AI RMF structure, functions, and implementation tiers
  • Ability to own framework decisions without requiring senior review
  • Decision-grade documentation templates for each AI risk assessment phase
  • Clear mapping from NIST guidance to enterprise risk appetite statements
  • Repeatable process for turning AI governance frameworks into actionable control artefacts

The 12 modules (with all 144 chapters)

Module 1. Understanding the NIST AI RMF Core
Break down the framework’s four functions, Map, Measure, Manage, Govern, and how they align to enterprise AI lifecycles.
12 chapters in this module
  1. Origins of the AI RMF
  2. Map function breakdown
  3. Measure function breakdown
  4. Govern function breakdown
  5. Core vs supplemental guidance
  6. Mapping to internal policies
  7. Framework version tracking
  8. Interpreting trustworthy AI
  9. Risk tolerance bands
  10. Use case scoping
  11. Stakeholder alignment
  12. Framework decision log
Module 2. Mapping AI Systems to Risk Context
Learn to classify AI applications by impact level, deployment context, and data sensitivity to determine appropriate oversight.
12 chapters in this module
  1. High-risk classification
  2. Autonomy level assessment
  3. Human oversight thresholds
  4. Data provenance tracking
  5. Third-party model sourcing
  6. Legacy integration risks
  7. Jurisdictional triggers
  8. Public-facing AI flags
  9. Impact tier assignment
  10. Risk context documentation
  11. Use case inventory
  12. Classification decision log
Module 3. Measuring AI Performance Against Intent
Establish performance baselines, monitor drift, and align model behavior with stated business objectives.
12 chapters in this module
  1. Intent articulation
  2. Performance benchmarks
  3. Model drift detection
  4. Bias threshold setting
  5. Human-in-the-loop design
  6. Explainability requirements
  7. Accuracy vs robustness
  8. Confidence interval tracking
  9. Feedback loop design
  10. Performance reporting
  11. Model card standards
  12. Validation frequency
Module 4. Managing AI Risks Across the Lifecycle
Apply risk treatment options, accept, modify, reject, monitor, and document justifications for executive review.
12 chapters in this module
  1. Risk treatment options
  2. Acceptance criteria
  3. Modification triggers
  4. Rejection protocols
  5. Ongoing monitoring
  6. Escalation paths
  7. Documentation standards
  8. Review cycles
  9. Change control process
  10. Versioning artefacts
  11. Cross-team alignment
  12. Decision traceability
Module 5. Governance Structures for AI Oversight
Design review boards, define roles, and codify escalation procedures for AI system approvals.
12 chapters in this module
  1. AI review board setup
  2. Membership criteria
  3. Charter definition
  4. Meeting cadence
  5. Decision authority
  6. Transparency commitments
  7. External reporting
  8. Audit readiness
  9. Stakeholder communication
  10. Policy enforcement
  11. Compliance tracking
  12. Board-level summary design
Module 6. Implementing Trustworthy AI Characteristics
Embed fairness, transparency, privacy, and security into AI systems from design through deployment.
12 chapters in this module
  1. Fairness metrics
  2. Bias mitigation steps
  3. Transparency levels
  4. Privacy-preserving methods
  5. Security-by-design
  6. Explainability approaches
  7. Human agency
  8. Robustness checks
  9. Resilience testing
  10. Audit trail design
  11. Model documentation
  12. System logs
Module 7. Integrating with Existing Risk Frameworks
Align NIST AI RMF with ISO 27001, SOC 2, COBIT, and internal enterprise risk management policies.
12 chapters in this module
  1. ISO 27001 mapping
  2. SOC 2 integration
  3. COBIT alignment
  4. ERM coordination
  5. Control overlap analysis
  6. Policy harmonization
  7. Cross-framework audit
  8. Unified reporting
  9. Risk register updates
  10. Compliance dashboards
  11. Stakeholder alignment
  12. Framework decision tracker
Module 8. Building Repeatable AI Risk Artefacts
Create standardized templates for AI inventories, risk assessments, and model impact statements.
12 chapters in this module
  1. AI inventory template
  2. Risk assessment form
  3. Model impact statement
  4. Decision justification log
  5. Third-party risk form
  6. Model deployment checklist
  7. Decommissioning protocol
  8. Version change log
  9. Stakeholder notification
  10. Audit trail structure
  11. Document retention
  12. Template governance
Module 9. Stakeholder Communication and Reporting
Develop clear messaging for executives, legal, compliance, and technical teams on AI risk posture.
12 chapters in this module
  1. Executive summary design
  2. Legal team briefing
  3. Compliance reporting
  4. Technical team alignment
  5. Public disclosure
  6. Regulator engagement
  7. Incident communication
  8. Stakeholder map
  9. Message tailoring
  10. Communication cadence
  11. Escalation messaging
  12. Crisis comms prep
Module 10. Auditing and Continuous Monitoring
Design audit plans, continuous monitoring rules, and automated alerts for AI system deviations.
12 chapters in this module
  1. Audit plan design
  2. Monitoring rules
  3. Automated alerts
  4. Drift detection
  5. Bias monitoring
  6. Performance tracking
  7. Human review triggers
  8. Log retention
  9. Audit trail access
  10. External auditor prep
  11. Findings response
  12. Remediation tracking
Module 11. Handling AI Incidents and Escalations
Define incident response workflows, escalation paths, and post-mortem processes for AI failures.
12 chapters in this module
  1. Incident definition
  2. Escalation path
  3. Response team
  4. Containment steps
  5. Investigation protocol
  6. Stakeholder notification
  7. Legal exposure
  8. Reputation management
  9. Post-mortem review
  10. Lessons learned
  11. Process update
  12. Public statement
Module 12. Scaling AI Governance Across the Enterprise
Extend governance practices across business units, geographies, and AI use cases with consistent rigor.
12 chapters in this module
  1. Central governance model
  2. Local implementation
  3. Global consistency
  4. Regional adaptation
  5. Cross-border rules
  6. Training rollout
  7. Adoption tracking
  8. Maturity assessment
  9. Continuous improvement
  10. Feedback integration
  11. Framework evolution
  12. Enterprise roadmap

How this maps to your situation

  • When starting a new AI governance initiative
  • Before an external audit cycle
  • After an AI incident or near miss
  • When scaling AI across business units

Before vs. after

Before
Relying on fragmented guidance and ad-hoc processes for AI risk decisions
After
Operating with structured command of the NIST AI RMF, producing consistent, defensible artefacts

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 completion over 6-8 weeks with on-demand access.

If nothing changes
Continuing with inconsistent AI risk practices could lead to audit findings, stakeholder misalignment, or escalations that slow innovation.

How this compares to the alternatives

Unlike generic AI ethics courses, this program focuses on executable mastery of the NIST AI RMF, giving you actionable control over real-world governance decisions.

Frequently asked

Who is this course designed for?
Senior governance leaders responsible for AI oversight in enterprise environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive practical tools?
Yes, every module includes downloadable templates and a final hand-built implementation playbook.
$199 one-time. Approximately 3 hours per module, designed for completion over 6-8 weeks with on-demand access..

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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