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Sources and specific examples on hand when peers push back

$199.00
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A tailored course, built for your situation

Sources and specific examples on hand when peers push back

Build unshakable reasoning behind AI governance decisions using NIST AI RMF

$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.
Peer challenges to AI governance decisions feel reactive because reasoning lacks traceable sources

The situation this course is for

Teams default to opinion in AI governance debates because they can’t quickly surface the source or precedent for a decision. This undermines authority and slows progress.

Who this is for

Senior AI governance practitioner advancing from implementation to influence

Who this is not for

Junior analysts looking for entry-level certification prep or generic AI overviews

What you walk away with

  • Map every NIST AI RMF function to at least two concrete implementation patterns
  • Document decision logic with inline citations to framework sections and external benchmarks
  • Annotate control trade-offs using regulator-adjacent language from AI Act and OECD AI Principles
  • Rebuild two legacy policy gaps using source-backed justification templates
  • Deliver a final reasoning dossier that survives cross-functional scrutiny

The 12 modules (with all 144 chapters)

Module 1. Introducing NIST AI RMF as a defensible foundation
Establish why NIST AI RMF is becoming the reference point for auditors and cross-functional reviewers. Understand its structure, intent, and how it differentiates from adjacent frameworks like AI Act and OECD AI Principles.
12 chapters in this module
  1. What NIST AI RMF solves that ISO 27001 does not
  2. Core components of the framework
  3. Mapping functions to real governance decisions
  4. How regulators cite the RMF in reviews
  5. Comparing RMF to AI Act requirements
  6. OECD principles as supporting logic
  7. When to use NIST over other standards
  8. Framework adoption patterns right now
  9. Traceability as a governance advantage
  10. Building credibility through citations
  11. Common misinterpretations to avoid
  12. First steps in internal alignment
Module 2. Govern through documented reasoning, not consensus
Shift from group agreement to structured justification. Learn how to preempt challenges by building source-backed logic into every governance decision.
12 chapters in this module
  1. Why consensus fails under scrutiny
  2. Decision logs with embedded citations
  3. Preempting pushback with evidence trails
  4. Using NIST subsections as anchors
  5. How to reference external benchmarks
  6. Writing justifications regulators accept
  7. Avoiding vague 'best practice' claims
  8. Building credibility across teams
  9. Template: Decision justification matrix
  10. Annotating trade-offs clearly
  11. When to escalate with documentation
  12. Keeping reasoning agile
Module 3. Map Govern function to policy design
Turn the NIST AI RMF Govern function into actionable policy structures. Show exactly how oversight mechanisms translate into controls.
12 chapters in this module
  1. Govern function at a glance
  2. Three types of oversight frameworks
  3. Mapping to internal audit cycles
  4. Designing for board-level clarity
  5. Linking to compliance calendars
  6. Integrating with risk registers
  7. Policy versioning with traceability
  8. Handling exemption requests
  9. Documenting escalation paths
  10. Benchmarking against top quartile teams
  11. Using AI Act Article 9 as reference
  12. Template: Policy traceability table
Module 4. Map Map function to data provenance controls
Anchor data lineage decisions in NIST AI RMF Mapping expectations. Build justification for cataloging rules, labeling thresholds, and metadata requirements.
12 chapters in this module
  1. Mapping data to AI impact levels
  2. Defining data provenance scope
  3. Justifying metadata completeness
  4. Linking to Unity Catalog design
  5. Cross-referencing with SOC 2
  6. Thresholds for manual review
  7. Handling third-party data sources
  8. Documentation for external auditors
  9. Using NIST 800-53 as parallel source
  10. Trade-off: granularity vs maintainability
  11. Template: Data classification matrix
  12. Case study: high-risk model input
Module 5. Map Train function to model development guardrails
Defend model training constraints with reference to NIST AI RMF training expectations. Justify data splitting, augmentation limits, and hyperparameter bounds.
12 chapters in this module
  1. Training data representativeness
  2. Bias mitigation thresholds
  3. Defensible augmentation rules
  4. Hyperparameter constraint logic
  5. Cross-validation design choices
  6. Version control for training sets
  7. Reproducibility as audit requirement
  8. Using MLOps logs as evidence
  9. Justifying retraining triggers
  10. Documenting model drift thresholds
  11. Template: Training guardrail dossier
  12. Case study: financial risk model
Module 6. Map Test function to validation design
Justify test design choices using NIST AI RMF testing expectations. Show how thresholds, edge case coverage, and adversarial testing were defined.
12 chapters in this module
  1. Defining performance baselines
  2. Selecting fairness metrics
  3. Edge case identification strategy
  4. Adversarial testing scope
  5. Interpreting AI Act high-risk tests
  6. Linking to model risk management
  7. Third-party validation prep
  8. Handling false negative tolerance
  9. Benchmarking against peer models
  10. Template: Validation justification log
  11. Case study: credit scoring test
  12. Documentation for external review
Module 7. Map Deploy function to production controls
Defend deployment decisions with clear mapping to NIST AI RMF deployment expectations. Clarify rollback triggers, monitoring scope, and human-in-the-loop rules.
12 chapters in this module
  1. Production readiness checklists
  2. Rollback trigger definitions
  3. Monitoring coverage thresholds
  4. Human oversight requirements
  5. Incident escalation design
  6. Linking to ISO 27001 controls
  7. Documentation for operations teams
  8. Justifying alerting thresholds
  9. Trade-off: velocity vs safety
  10. Template: Deployment sign-off log
  11. Case study: real-time scoring
  12. Handling emergency overrides
Module 8. Map Monitor function to feedback loops
Justify ongoing monitoring design using NIST AI RMF monitoring expectations. Show how feedback collection, drift detection, and user reports inform updates.
12 chapters in this module
  1. Designing feedback ingestion
  2. Defining drift detection frequency
  3. User report handling workflows
  4. Linking to customer support
  5. Automated alert thresholds
  6. Manual review cadence
  7. Justifying update triggers
  8. Documentation for compliance
  9. Benchmarking against industry norms
  10. Template: Monitoring decision log
  11. Case study: e-commerce recommender
  12. Handling silent failure
Module 9. Map Secure function to AI-specific threats
Defend security controls by mapping to NIST AI RMF Secure function. Clarify how model inversion, prompt injection, and data poisoning were addressed.
12 chapters in this module
  1. AI-specific threat categories
  2. Model inversion mitigations
  3. Prompt injection defenses
  4. Data poisoning detection
  5. Access control for model endpoints
  6. Authentication for API calls
  7. Logging for forensic traceability
  8. Linking to NIST CSF
  9. Threat modeling for AI systems
  10. Template: AI threat register
  11. Case study: chatbot exposure
  12. Documentation for auditors
Module 10. Cross-walk with AI Act and OECD principles
Strengthen defensibility by showing alignment with other major frameworks. Use AI Act and OECD AI Principles to reinforce NIST-based decisions.
12 chapters in this module
  1. AI Act high-risk criteria
  2. Mapping NIST to Article 9 requirements
  3. OECD principle 1: Inclusive growth
  4. OECD principle 2: Human-centered values
  5. OECD principle 3: Transparency
  6. OECD principle 4: Robustness
  7. OECD principle 5: Accountability
  8. Using OECD as supporting logic
  9. Handling conflicting requirements
  10. Template: Cross-framework alignment
  11. Case study: healthcare AI
  12. When to defer to AI Act
Module 11. Build defensible decision logs
Create decision logs that survive peer review. Embed citations, thresholds, and trade-offs so the team can move forward without re-litigating.
12 chapters in this module
  1. Structure of a defensible log
  2. Including framework references
  3. Annotating risk acceptance
  4. Versioning alongside models
  5. Linking to Jira tickets
  6. Using ServiceNow for traceability
  7. Making logs review-ready
  8. Template: Decision log format
  9. Case study: model approval
  10. Handling leadership challenges
  11. Automating log updates
  12. Archiving for audits
Module 12. Deliver a defensibility dossier
Compile a final dossier that ties all decisions to NIST AI RMF, AI Act, and OECD sources. Create a reference artifact for cross-functional teams and auditors.
12 chapters in this module
  1. Dossier structure overview
  2. Executive summary with evidence
  3. Detailed decision appendices
  4. Cross-reference index
  5. Version control strategy
  6. Distribution to stakeholders
  7. Updating across model versions
  8. Using the dossier in audits
  9. Template: Dossier cover sheet
  10. Case study: regulator review
  11. Maintaining credibility long-term
  12. Handing off to successor

How this maps to your situation

  • When introducing AI governance to skeptical teams
  • During auditor inquiries on control design
  • Before signing off on a high-risk model
  • While defending a rejected proposal in review

Before vs. after

Before
Relies on internal consensus and informal justification for AI governance decisions
After
Walks into every review with documented reasoning, clear citations, and specific examples ready

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 real-world application alongside current projects.

If nothing changes
Continuing to depend on group agreement leaves decisions vulnerable to challenge, slows adoption, and risks erosion of influence when leadership scrutiny increases.

How this compares to the alternatives

Unlike generic AI governance overviews or certification prep, this course focuses exclusively on building defensible reasoning using live frameworks and real implementation patterns. No fluff, no abstractions, only source-backed logic you can use Monday morning.

Frequently asked

Is this course focused on technical implementation?
It’s focused on defensible reasoning behind technical and governance decisions. You’ll learn how to justify implementation choices using NIST AI RMF, AI Act, and OECD sources.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I use this alongside my current projects?
Yes, each module includes templates and examples designed to plug directly into real governance work.
$199 one-time. Approximately 3 hours per module, designed for real-world application alongside 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
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