A tailored course, built for your situation
Board-Level AI Audit Readiness for Compliance Officers
Master the governance frameworks and technical fluency needed to lead AI compliance at the executive level
$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.
Feeling unprepared when AI compliance questions escalate to governance committees or auditors?
The situation this course is for
Compliance officers are increasingly asked to validate AI systems without clear frameworks, consistent documentation standards, or direct lines to technical teams. This gap slows approvals, increases review cycles, and limits strategic influence.
Who this is for
Mid-to-senior compliance, risk, and governance professionals guiding AI oversight in regulated environments
Who this is not for
Individuals seeking introductory AI literacy or general data protection training
What you walk away with
- Lead AI audit preparation with confidence using board-ready frameworks
- Translate technical AI artifacts into compliance evidence
- Align model governance practices with emerging regulatory expectations
- Communicate effectively with data science teams and executive stakeholders
- Deploy a repeatable AI compliance playbook within your organization
The 12 modules (with all 144 chapters)
Module 1. AI Governance in the Compliance Landscape
Understand the shift toward board-level AI oversight and the expanding role of compliance professionals
12 chapters in this module
- Defining AI compliance maturity
- Mapping regulatory drivers
- Board expectations on AI risk
- Compliance’s role in AI lifecycle
- Linking AI to ERM frameworks
- Industry benchmarks in AI governance
- Stakeholder alignment models
- AI accountability structures
- Compliance leadership pathways
- Emerging standards and frameworks
- Auditor expectations on AI
- Building cross-functional credibility
Module 2. AI Audit Frameworks and Standards
Explore established and emerging frameworks for auditing AI systems
12 chapters in this module
- NIST AI RMF fundamentals
- OECD AI Principles alignment
- ISO/IEC 42001 overview
- EU AI Act compliance layers
- Sector-specific audit requirements
- Internal vs external audit scope
- Audit readiness scoring models
- Third-party assessment criteria
- Documenting AI controls
- Evidence collection strategies
- Audit trail design for models
- Versioning and change tracking
Module 3. Model Risk Management Integration
Adapt traditional model risk frameworks to AI-specific challenges
12 chapters in this module
- Extending MRAs to machine learning
- Defining AI model inventory scope
- Model categorization by risk tier
- Validation expectations for AI
- Performance drift detection
- Bias and fairness testing
- Explainability requirements
- Model documentation standards
- Retraining audit triggers
- Model decommissioning protocols
- Lifecycle governance workflows
- Model lineage tracking
Module 4. Regulatory Alignment Strategies
Align AI compliance efforts with current regulatory expectations
12 chapters in this module
- FDA AI/ML guidance in healthcare
- HIPAA implications for AI
- OCR oversight trends
- Joint Commission considerations
- State-level AI regulations
- Federal enforcement priorities
- Privacy-preserving AI techniques
- Consent and data provenance
- Algorithmic transparency rules
- Patient impact assessments
- Compliance reporting cadence
- Regulator engagement protocols
Module 5. AI Documentation and Evidence
Build comprehensive, auditor-friendly documentation packages
12 chapters in this module
- AI model cards design
- System specification templates
- Data lineage documentation
- Training data provenance
- Validation report structure
- Bias assessment records
- Explainability reports
- Performance monitoring logs
- Change control documentation
- Third-party model oversight
- Vendor AI compliance checks
- Audit response preparation
Module 6. Cross-Functional Collaboration
Lead AI compliance initiatives across technical and non-technical teams
12 chapters in this module
- Translating compliance needs to data science
- Engaging legal and privacy teams
- Working with clinical stakeholders
- Facilitating ethics reviews
- Managing vendor relationships
- Building AI governance committees
- Conflict resolution frameworks
- Influencing without authority
- Running AI compliance workshops
- Creating feedback loops
- Driving accountability across silos
- Scaling compliance influence
Module 7. Bias and Fairness Audits
Conduct rigorous fairness assessments aligned with compliance standards
12 chapters in this module
- Defining fairness in healthcare AI
- Protected class identification
- Disparity impact measurement
- Pre-processing bias detection
- In-model fairness techniques
- Post-processing adjustments
- Segmented performance analysis
- Clinical impact disparity
- Fairness reporting standards
- Bias mitigation documentation
- Stakeholder fairness expectations
- Ongoing fairness monitoring
Module 8. Explainability and Transparency
Ensure AI decisions are interpretable and defensible
12 chapters in this module
- Regulatory expectations on explainability
- Model interpretability tiers
- SHAP and LIME applications
- Counterfactual explanations
- Local vs global interpretability
- Documentation of rationale
- Clinician-facing summaries
- Patient communication strategies
- Trade-offs with model performance
- Explainability in audit responses
- Transparency reporting
- Managing black-box models
Module 9. AI Incident Response Planning
Prepare for AI-related incidents with structured compliance protocols
12 chapters in this module
- Defining AI incidents and near misses
- Incident classification frameworks
- Notification requirements
- Root cause investigation
- Documentation preservation
- Regulatory reporting triggers
- Internal escalation paths
- Corrective action planning
- Model rollback procedures
- Post-mortem compliance review
- Lessons learned integration
- Incident simulation drills
Module 10. Vendor AI Oversight
Extend compliance rigor to third-party AI solutions
12 chapters in this module
- Vendor AI due diligence
- Contractual compliance terms
- Audit rights negotiation
- Third-party model validation
- API security assessment
- Performance monitoring SLAs
- Data handling compliance
- Model change notifications
- Exit strategy planning
- Multi-vendor coordination
- Compliance escalation paths
- Vendor risk tiering
Module 11. Executive Communication and Reporting
Present AI compliance status effectively to leadership and boards
12 chapters in this module
- Board-level reporting cadence
- AI risk dashboard design
- Executive summary writing
- Translating technical findings
- Risk appetite alignment
- Compliance maturity scoring
- Strategic initiative framing
- Budget justification narratives
- AI policy recommendation
- Crisis communication planning
- Stakeholder briefing templates
- Metrics that matter to governance
Module 12. Sustaining AI Compliance at Scale
Build systems to maintain compliance as AI adoption grows
12 chapters in this module
- Compliance automation opportunities
- AI governance tooling
- Centralized model registry
- Continuous monitoring design
- Compliance training programs
- Internal audit coordination
- Policy refresh cycles
- Regulatory horizon scanning
- Lessons learned integration
- Scaling governance teams
- Compliance culture development
- Future-proofing strategies
How this maps to your situation
- Preparing for first AI audit
- Responding to regulatory inquiry
- Scaling AI governance across departments
- Transitioning from pilot to production oversight
Before vs. after
Before
Uncertain about how to structure AI compliance efforts or respond to auditor questions about model governance
After
Equipped with a board-ready framework, clear documentation standards, and a repeatable process for AI audit readiness
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 busy professionals. Total investment: 36 hours, self-paced.
If nothing changes
Organizations without structured AI compliance practices may face delayed approvals, increased regulatory scrutiny, and reduced trust in AI-driven initiatives.
How this compares to the alternatives
Unlike generic AI awareness courses or technical data science programs, this course is tailored specifically for compliance professionals who must demonstrate governance rigor without needing to code or build models.
Frequently asked
Who is this course designed for?
Compliance, risk, and governance professionals responsible for AI oversight in regulated environments, especially those engaging with auditors or board-level committees.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Do I need a technical background?
No. The course is designed for compliance professionals who need to understand AI systems at a governance level, not build or code them.
$199 one-time. Approximately 3 hours per module, designed for busy professionals. Total investment: 36 hours, self-paced..
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