A tailored course, built for your situation
Compliance-Ready AI Model Risk Management for Senior Leaders
Implement AI governance with confidence using board-ready frameworks and operational playbooks
$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.
Even well-intentioned AI initiatives stall without clear compliance pathways and executable risk controls.
The situation this course is for
Leaders are expected to govern AI systems they didn’t build, using standards that keep evolving. Without a structured, repeatable method, oversight becomes reactive, fragmented, and vulnerable to audit findings or reputational exposure.
Who this is for
Senior leaders in compliance, risk, IT governance, or technology oversight roles who influence or direct AI model deployment and monitoring.
Who this is not for
Individual contributors focused only on model development, data scientists without governance responsibilities, or teams seeking certification prep only.
What you walk away with
- Apply a compliance-first lens to AI model lifecycle oversight
- Structure model risk assessments that satisfy internal and external auditors
- Align AI governance with existing regulatory frameworks (e.g., NIST, SEC, OCR)
- Lead cross-functional teams with clear, documented controls and accountability
- Deploy a living model risk management framework that scales with AI adoption
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Model Risk
Define risk in the context of AI systems and establish governance boundaries.
12 chapters in this module
- Defining AI model risk
- Governance vs. technical debt
- Regulatory touchpoints
- Stakeholder mapping
- Risk taxonomy
- Model inventory design
- Lifecycle phases
- Control points
- Audit readiness
- Documentation standards
- Change management
- Escalation protocols
Module 2. Compliance Framework Integration
Map AI model practices to existing compliance standards.
12 chapters in this module
- NIST AI RMF alignment
- OCR and FERPA considerations
- SEC disclosure rules
- State-level education mandates
- GDPR parallels
- Internal policy mapping
- Gap analysis method
- Control harmonization
- Audit trail design
- Evidence collection
- Reporting cadence
- Third-party oversight
Module 3. Model Validation Oversight
Ensure models perform as intended without bias or drift.
12 chapters in this module
- Validation scope definition
- Bias detection frameworks
- Fairness metrics
- Performance thresholds
- Backtesting methods
- Sensitivity analysis
- Drift monitoring
- Version control
- Human-in-the-loop design
- Escalation triggers
- Calibration checks
- Validation documentation
Module 4. Risk Assessment Methodology
Apply structured scoring to AI model deployments.
12 chapters in this module
- Risk scoring framework
- Impact likelihood matrix
- High-risk designation
- Use case classification
- Data sensitivity tiers
- Model complexity index
- External dependency risk
- Reputational exposure
- Operational disruption
- Legal liability
- Remediation planning
- Risk register maintenance
Module 5. Governance Structure Design
Build oversight committees and decision rights.
12 chapters in this module
- AI governance board setup
- Charter development
- Membership roles
- Meeting cadence
- Decision logs
- Escalation paths
- Cross-functional alignment
- Policy approval workflow
- Stakeholder engagement
- Transparency reporting
- Vendor governance
- Third-party model oversight
Module 6. Model Lifecycle Controls
Embed risk checks at every stage of deployment.
12 chapters in this module
- Pre-deployment checklist
- Change approval process
- Version rollback plan
- Monitoring integration
- Decommissioning protocol
- Data lineage tracking
- Model retraining rules
- Performance alerts
- Incident response
- Post-mortem process
- Lifecycle audit trail
- Retention policy
Module 7. Documentation and Audit Readiness
Create inspectable records for internal and external review.
12 chapters in this module
- Model documentation standards
- Regulatory evidence packs
- Versioned artifact storage
- Audit response workflow
- Document retention rules
- Access control for records
- External auditor prep
- Findings remediation
- Internal review cycle
- Compliance dashboards
- Reporting templates
- Evidence automation
Module 8. Bias and Fairness Governance
Operationalize fairness across model design and use.
12 chapters in this module
- Bias definition framework
- Protected class identification
- Disparity testing
- Fairness metrics selection
- Impact analysis
- Remediation protocols
- Stakeholder feedback
- Bias audit planning
- Transparency reporting
- Community engagement
- Bias mitigation tracking
- Ongoing monitoring
Module 9. Third-Party and Vendor Model Oversight
Extend governance to external AI systems.
12 chapters in this module
- Vendor due diligence
- Contractual risk clauses
- Model access rights
- Performance SLAs
- Audit rights
- Data handling compliance
- Sub-processor oversight
- Model explainability from vendors
- Incident notification
- Exit strategy
- Vendor scorecards
- Ongoing monitoring
Module 10. Incident Response and Escalation
Prepare for model failures with clear protocols.
12 chapters in this module
- Incident classification
- Response team roles
- Communication plan
- Regulatory reporting
- Root cause analysis
- Remediation tracking
- Public statement prep
- Legal counsel coordination
- Post-mortem documentation
- System improvements
- Stakeholder updates
- Lessons learned
Module 11. Change Management and Adoption
Drive organizational alignment on AI risk practices.
12 chapters in this module
- Stakeholder onboarding
- Training rollout plan
- Policy communication
- Feedback loops
- Adoption metrics
- Resistance mapping
- Champion network
- Knowledge transfer
- Role clarity
- Incentive alignment
- Progress tracking
- Culture assessment
Module 12. Sustaining and Scaling the Framework
Evolve governance as AI use grows.
12 chapters in this module
- Maturity model application
- Continuous improvement
- Benchmarking
- Resource planning
- Budget alignment
- Staffing models
- Technology enablement
- Framework updates
- External trend monitoring
- Lessons integration
- Scaling playbook
- Leadership transition
How this maps to your situation
- New AI initiative launch
- Post-audit remediation
- Regulatory change adoption
- Third-party model integration
Before vs. after
Before
Uncertainty about how to structure AI oversight in a compliant, auditable way
After
Clarity on building and maintaining a defensible, board-ready AI model risk framework
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 self-paced completion over 12 weeks.
If nothing changes
Without a structured approach, organizations face inconsistent oversight, audit exposure, and reputational risk as AI use expands without guardrails.
How this compares to the alternatives
Unlike generic AI ethics courses or technical model monitoring tools, this course delivers a compliance-first, implementation-grade framework tailored for senior leaders who must answer to boards, auditors, and regulators.
Frequently asked
Who is this course designed for?
Senior leaders in compliance, risk, governance, or technology oversight roles who are responsible for AI model accountability.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there live instruction or video content?
No. The course is text-based with downloadable resources and a hand-built implementation playbook.
$199 one-time. Approximately 3 hours per module, designed for self-paced completion 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