A tailored course, built for your situation
Deeper command of AI governance frameworks for financial services
Master the structure, standards, and strategic application of AI governance in regulated banking environments
$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.
The situation this course is for
Who this is for
Senior governance practitioner in a regulated financial institution, responsible for designing or overseeing AI/ML oversight frameworks, model risk policy, or emerging technology controls
Who this is not for
Entry-level compliance analysts, data scientists without governance responsibilities, or vendors selling AI tools without regulatory implementation experience
What you walk away with
- Full command of NIST AI RMF, ISO/IEC 42001, and FRB SR 11-7 integration points
- Ability to map controls to model lifecycle stages with precision and audit-ready logic
- Templates for governance charter, risk tiering matrices, and escalation protocols
- Pre-built rationale libraries for high-stakes decisions (e.g. high-risk model classification)
- Strategic fluency to lead cross-functional alignment between legal, risk, and AI delivery teams
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI governance in regulated finance
Establish the core principles differentiating AI governance from traditional model risk management, with emphasis on dynamic systems, opacity, and feedback loops unique to machine learning.
12 chapters in this module
- What makes AI governance distinct
- Regulatory evolution since the current cycle
- Three pillars of financial AI oversight
- Model vs system-level controls
- Lifecycle-aware governance design
- Risk tiers and materiality thresholds
- Linking governance to capital planning
- Key differences from fintech models
- Scope definition without overreach
- Boundary decisions: AI vs automation
- First artefact: governance boundary map
- Establishing your baseline framework
Module 2. NIST AI RMF deep integration
Break down the NIST AI Risk Management Framework into executable components, showing how each function maps to existing bank control environments and where gaps typically emerge.
12 chapters in this module
- Mapping Govern to policy ownership
- Scoping for realism and coverage
- Mapping risks to NIST categories
- Tailoring to bank-specific threats
- Profile creation: current vs target
- Implementation tiers in practice
- Integrating with FFIEC guidance
- Crosswalk to internal audit standards
- Using AI RMF in vendor assessments
- Documentation standards for examiners
- Second artefact: tailored AI RMF profile
- Maintaining version control
Module 3. ISO/IEC 42001 control mapping
Translate ISO’s AI management system into operational workflows, demonstrating how certification-grade controls can be adapted for internal governance without full audit burden.
12 chapters in this module
- Clause-by-clause breakdown
- AISMS vs traditional ISMS
- Control selection rationale
- Documented information requirements
- Competence evidence for teams
- Internal audit preparation
- Management review inputs
- Nonconformity handling workflows
- Linking to model validation reports
- Automating evidence collection
- Third artefact: control mapping table
- Gap heatmap for leadership review
Module 4. SR 11-7 and model risk alignment
Align AI governance activities with Federal Reserve guidance, ensuring consistency with existing Model Risk Management frameworks while accommodating AI-specific risks.
12 chapters in this module
- Scope overlap with AI systems
- Validation expectations for black boxes
- Ongoing monitoring adaptations
- Benchmarking alternative approaches
- Explainability as a control
- Backtesting limitations and workarounds
- Challenge function integration
- Inventory classification rules
- Fourth artefact: AI model tiering policy
- Documentation trail design
- Coordination with Chief Model Officer
- Preparing for horizontal reviews
Module 5. Governance charter and operating model
Design a governance charter that defines authority, escalation paths, and decision rights, aligned to organizational structure and risk appetite statements.
12 chapters in this module
- Charter purpose and audience
- Stakeholder mapping exercise
- Decision rights allocation
- Escalation thresholds by risk level
- Meeting cadence and outputs
- Resource planning assumptions
- Fifth artefact: governance charter draft
- Operating model diagrams
- RACI for AI oversight
- Integrating with ERM frameworks
- Change control for policy updates
- Versioning and approval workflow
Module 6. Risk tiering and classification systems
Build a defensible, transparent system for classifying AI applications by risk level, incorporating materiality, harm potential, and regulatory scrutiny.
12 chapters in this module
- Dimensions of AI risk assessment
- Harm typology for financial services
- Materiality scoring methodology
- Customer impact weighting
- Systemic risk considerations
- Regulatory attention indicators
- Sixth artefact: risk tiering matrix
- Automation vs human oversight rules
- Review frequency by tier
- Appeal and reassessment process
- Documentation standards
- Change triggers for reclassification
Module 7. Control design for high-risk models
Develop targeted controls for high-risk AI systems, including pre-deployment checks, monitoring KPIs, and fail-safes, with examples from credit, fraud, and servicing use cases.
12 chapters in this module
- Pre-deployment checklist design
- Performance monitoring thresholds
- Drift detection protocols
- Bias testing methodology
- Fallback mechanism requirements
- Explainability integration
- Adversarial testing planning
- Incident response playbooks
- Seventh artefact: high-risk model playbook
- Control testing procedures
- Audit trail completeness
- Third-party validation planning
Module 8. Explainability as a governance tool
Apply explainability techniques not just for compliance, but as active governance levers to improve model understanding, stakeholder trust, and challenge readiness.
12 chapters in this module
- Beyond SHAP and LIME
- Business-friendly explanation formats
- Target audience segmentation
- Integration into model documentation
- Challenge function support materials
- Regulator communication templates
- Eighth artefact: explanation package
- Automated summary generation
- Version-controlled rationale
- Feedback loop with developers
- Handling unexplainable models
- Documentation for edge cases
Module 9. Vendor and third-party oversight
Implement rigorous oversight of external AI providers, including due diligence, contract clauses, and ongoing monitoring, tailored to financial services risk posture.
12 chapters in this module
- Vendor risk classification
- Due diligence checklist
- Contractual control requirements
- Audit rights negotiation
- Ninth artefact: third-party assessment form
- Ongoing monitoring metrics
- Performance penalty design
- Exit strategy planning
- Subprocessor oversight
- Incident notification protocols
- Compliance attestation handling
- Relationship governance model
Module 10. Cross-functional alignment strategies
Lead alignment between legal, compliance, risk, data science, and business units using structured communication tools and shared artefacts that reduce friction and clarify ownership.
12 chapters in this module
- Identifying alignment bottlenecks
- Shared vocabulary development
- Tenth artefact: stakeholder briefing deck
- Decision log transparency
- Conflict resolution protocol
- Policy feedback mechanism
- Alignment workshop design
- Escalation handling scripts
- Cross-team RACI refinement
- Feedback integration process
- Status reporting cadence
- Governance ambassador program
Module 11. Regulator engagement preparation
Prepare for supervisory reviews with artefacts and reasoning patterns that demonstrate proactive, principles-based governance, reducing examination surprises and follow-ups.
12 chapters in this module
- Common examiner questions
- Evidence package structure
- Position paper drafting
- Eleventh artefact: regulator-ready briefing
- Mock examination process
- Response protocol design
- Coordination with legal counsel
- Issue tracking and resolution
- Lessons from recent exams
- Communication escalation paths
- Document hold procedures
- Post-review action planning
Module 12. Sustaining and evolving the framework
Ensure long-term relevance and adaptability of the AI governance framework through feedback loops, horizon scanning, and iterative improvement processes.
12 chapters in this module
- Feedback collection mechanisms
- Change impact assessment
- Twelfth artefact: governance roadmap
- Horizon scanning process
- Regulatory change tracking
- Technology trend monitoring
- Lessons learned integration
- Framework review cadence
- Stakeholder satisfaction survey
- Benchmarking against peers
- Continuous improvement cycle
- Sunsetting outdated controls
How this maps to your situation
- Designing or updating an AI governance framework
- Responding to internal audit or regulatory findings
- Scaling AI initiatives across the enterprise
- Leading cross-functional governance coordination
Before vs. after
Before
Reliance on fragmented guidance, inconsistent application of standards, and reactive responses to governance challenges
After
Command of integrated frameworks, proactive control design, and confident leadership in AI governance decisions
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 3-4 hours per module, designed for completion over 6-8 weeks with practical application between modules.
How this compares to the alternatives
Unlike generic AI ethics courses or academic treatments, this program focuses on actionable governance artefacts, regulatory alignment, and real-world implementation in complex financial institutions.
Frequently asked
Is this course technical or policy-focused?
It is policy and governance-focused, designed for risk, compliance, and oversight leaders who need to direct technical teams without being hands-on coders.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me prepare for regulatory exams?
Yes, modules include artefacts and documentation strategies specifically designed to meet examiner expectations and reduce follow-up requests.
$199 one-time. Approximately 3-4 hours per module, designed for completion over 6-8 weeks with practical application between modules..
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