A tailored course, built for your situation
Practical AI Model Risk Management for Audit Teams
Implement risk-aware AI governance with confidence and precision
$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.
Lack of clear, actionable frameworks for auditing AI models creates friction, rework, and uncertainty during compliance cycles.
The situation this course is for
Audit teams are increasingly asked to assess AI-driven systems without standardized tools or playbooks. Traditional risk checklists don’t map cleanly to model behavior, versioning, or data drift. This leads to inconsistent evaluations, last-minute escalations, and over-reliance on technical teams who may not speak the language of control.
Who this is for
Business and technology professionals in risk, compliance, audit, or governance roles who work alongside data science or AI teams and need to establish credible, repeatable review practices.
Who this is not for
This course is not for data scientists building models, nor for executives seeking high-level AI strategy. It’s for practitioners who implement and validate controls.
What you walk away with
- Apply a structured approach to AI model risk assessment aligned with audit expectations
- Use standardized templates to document model controls and validation steps
- Identify critical failure points in model lifecycle management
- Coordinate effectively between technical teams and audit functions
- Produce audit-ready artifacts that stand up to scrutiny
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Model Risk
Define core concepts including model risk, governance boundaries, and audit scope.
12 chapters in this module
- Understanding model risk in context
- Mapping AI use cases to risk levels
- Key roles in model governance
- Regulatory drivers and expectations
- Model lifecycle overview
- Control objectives for AI systems
- Risk-based prioritization framework
- Documentation fundamentals
- Versioning and traceability
- Model inventory design
- Change management for models
- Baseline assessment template
Module 2. Control Design for AI Systems
Build effective controls tailored to AI model behavior and deployment patterns.
12 chapters in this module
- Types of model controls
- Input validation strategies
- Output monitoring techniques
- Model drift detection design
- Bias and fairness guardrails
- Explainability as a control
- Access control integration
- Model rollback procedures
- Testing control effectiveness
- Control ownership models
- Automation opportunities
- Control documentation standards
Module 3. Model Validation Frameworks
Implement repeatable validation processes for technical and non-technical reviewers.
12 chapters in this module
- Validation vs verification
- Pre-deployment review checklist
- Model performance thresholds
- Backtesting methods
- Stress testing scenarios
- Sensitivity analysis execution
- Validation of explainability tools
- Third-party model review
- Validation documentation
- Version-to-version comparison
- Revalidation triggers
- Validation workflow integration
Module 4. Documentation for Audit Readiness
Create clear, consistent records that support audit inquiry.
12 chapters in this module
- Audit trail requirements
- Model development history tracking
- Data lineage documentation
- Assumptions and limitations logging
- Model decision rationale
- Version change log format
- Model decommissioning records
- Evidence collection workflow
- Standardized naming conventions
- Centralized documentation hub
- Access and retention policies
- Audit response preparation
Module 5. Cross-Functional Coordination
Align data science, compliance, and audit teams around shared standards.
12 chapters in this module
- Stakeholder identification
- Communication cadence design
- Handoff protocols between teams
- Glossary alignment sessions
- Joint risk assessment workshops
- Escalation pathways
- Feedback loops for model issues
- Shared documentation platforms
- Role clarity in model lifecycle
- Conflict resolution frameworks
- Training for non-technical reviewers
- Metrics for collaboration success
Module 6. Model Inventory Management
Maintain a dynamic, accurate registry of AI models in production.
12 chapters in this module
- Model registry design principles
- Attributes to track in inventory
- Ownership assignment process
- Integration with IT asset systems
- Automated discovery methods
- Classification by risk tier
- Model sunset tracking
- Change notification workflows
- Audit status indicators
- Reporting from inventory data
- Third-party model inclusion
- Inventory maintenance SOP
Module 7. Risk Tiering and Prioritization
Apply risk-based segmentation to focus efforts where it matters most.
12 chapters in this module
- Impact vs likelihood matrix
- Financial exposure scoring
- Reputational risk assessment
- Operational disruption levels
- Customer harm potential
- Regulatory scrutiny bands
- Model complexity scoring
- Data sensitivity classification
- Scoring calibration process
- Tier-based control intensity
- Dynamic re-tiering triggers
- Risk register integration
Module 8. Model Monitoring in Production
Establish ongoing surveillance to detect model degradation or drift.
12 chapters in this module
- Performance metric selection
- Statistical process control for models
- Concept drift detection
- Data drift detection
- Threshold setting methodology
- Alerting configuration
- Model health dashboards
- Human-in-the-loop review
- Automated remediation paths
- Model degradation response
- Monitoring coverage audit
- Monitoring documentation
Module 9. Model Retraining and Updates
Govern iterative changes to models without compromising control integrity.
12 chapters in this module
- Retraining trigger conditions
- Version control for models
- Change impact assessment
- Revalidation requirements
- Rollback preparedness
- Model lineage tracking
- Approval workflows
- Testing in staging environments
- Production deployment controls
- Post-deployment monitoring
- Update documentation
- Model retirement process
Module 10. Third-Party Model Risk
Assess and monitor external AI models and vendor-provided systems.
12 chapters in this module
- Vendor due diligence
- Contractual control rights
- Transparency requirements
- Audit rights negotiation
- Performance SLAs
- Data handling assurances
- Model access limitations
- Ongoing monitoring of vendor models
- Third-party validation reports
- Risk transfer considerations
- Exit strategy planning
- Vendor model inventory inclusion
Module 11. Audit Engagement Preparation
Streamline interactions with internal and external auditors.
12 chapters in this module
- Audit request response protocol
- Evidence package assembly
- Pre-audit walkthroughs
- Common auditor questions
- Control demonstration techniques
- Issue tracking and resolution
- Follow-up action management
- Audit finding classification
- Remediation planning
- Audit communication templates
- Lessons learned capture
- Continuous improvement loop
Module 12. Continuous Improvement
Refine model risk practices based on real-world feedback and outcomes.
12 chapters in this module
- Post-audit review process
- Model incident root cause analysis
- Control gap identification
- Feedback from auditors
- Benchmarking against peers
- Lessons learned repository
- Process update workflow
- Training updates
- Metrics for program maturity
- Stakeholder satisfaction surveys
- Annual risk assessment refresh
- Roadmap for future enhancements
How this maps to your situation
- Organizations deploying AI models without standardized audit controls
- Audit teams encountering AI systems for the first time
- Compliance officers building model risk frameworks
- Risk managers overseeing AI governance programs
Before vs. after
Before
Uncertainty about how to audit AI models, reliance on ad-hoc processes, inconsistent documentation, and misalignment across teams.
After
Structured, repeatable model risk practices that support confident audit outcomes and stronger cross-functional collaboration.
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 steady implementation alongside regular responsibilities.
If nothing changes
Without clear practices, audit cycles become more complex, findings increase, and trust in AI systems erodes, delaying broader adoption and value realization.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level strategy decks, this program delivers implementation-grade structure, actionable templates, and audit-specific workflows used in operating-grade organizations.
Frequently asked
Who is this course for?
This course is for risk, compliance, audit, and governance professionals who need to assess or manage AI model risk in practice.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a certificate of completion is issued after finishing all modules and assessments.
$199 one-time. Approximately 3-4 hours per module, designed for steady implementation alongside regular responsibilities..
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