A tailored course, built for your situation
Mid-Market AI Model Risk Management for Audit Teams
A structured implementation path for audit professionals navigating AI governance
$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.
Audit teams are expected to validate AI systems without clear frameworks or tools.
The situation this course is for
Mid-market organizations are adopting AI faster than governance practices can keep up. Audit professionals are being asked to assess model risk without standardized methods, clear documentation, or scalable processes. This creates friction, inconsistent findings, and missed opportunities to influence responsible AI adoption.
Who this is for
Compliance officers, internal auditors, risk analysts, and technology assurance professionals in mid-market firms implementing or reviewing AI-driven systems.
Who this is not for
This course is not for enterprise-scale AI ethics boards, academic researchers, or engineers building foundational models.
What you walk away with
- Apply a repeatable framework to assess AI model risk in audit contexts
- Document controls and validation steps that satisfy internal and external reviewers
- Differentiate between model types and adjust audit approach accordingly
- Integrate AI risk assessments into existing audit workflows
- Lead conversations with technical teams using precise, implementation-ready criteria
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI in the Mid-Market
Understand the AI landscape specific to mid-market adoption patterns and risk profiles.
12 chapters in this module
- Defining AI in business contexts
- Common use cases in mid-market firms
- Risk categories unique to scaled-down deployments
- Regulatory touchpoints without overcompliance
- Distinguishing between automation and intelligence
- Vendor-hosted vs in-house models
- Lifecycle stages of AI systems
- Stakeholder mapping for audit influence
- Aligning with board-level expectations
- Benchmarking maturity across peers
- Common failure patterns in small deployments
- Audit readiness assessment framework
Module 2. Model Risk Management Principles
Adapt core model risk concepts from financial services to broader AI applications.
12 chapters in this module
- Origins of model risk management
- Key tenets from FRB SR 11-7
- Translating MRM to non-financial models
- Governance vs control vs oversight
- Roles and responsibilities matrix
- Documentation standards for auditors
- Versioning and change tracking
- Model inventory design
- Risk rating methodologies
- Thresholds for escalation
- Integration with existing risk frameworks
- Audit trail expectations
Module 3. AI Audit Planning and Scoping
Define the scope, objectives, and methodology for AI-focused audits.
12 chapters in this module
- Identifying high-risk AI applications
- Scoping criteria for limited resources
- Engagement planning checklist
- Stakeholder interview protocols
- Data source validation strategies
- Model purpose alignment review
- Output sensitivity analysis
- Deployment environment assessment
- Third-party model reliance
- Integration points with core systems
- Timeboxing complex reviews
- Deliverable templates for early alignment
Module 4. Data Quality and Provenance Audit
Assess the integrity, lineage, and representativeness of training and input data.
12 chapters in this module
- Data lifecycle mapping
- Provenance documentation review
- Bias indicators in dataset composition
- Missing data handling protocols
- Feature engineering transparency
- Data refresh and staleness checks
- Labeling consistency audits
- Synthetic data validation
- PII and privacy safeguards
- Drift detection mechanisms
- Access control verification
- Chain of custody for audit evidence
Module 5. Model Validation Techniques
Apply validation methods appropriate to model type, complexity, and risk level.
12 chapters in this module
- Validation vs verification distinction
- Performance metric audit trails
- Baseline comparison strategies
- Backtesting feasibility in non-financial models
- Sensitivity analysis execution
- Stress testing lightweight models
- Cross-validation documentation review
- Error analysis patterns
- Confidence interval validation
- Model stability over time
- Validation of generative outputs
- Third-party validation coordination
Module 6. Bias, Fairness, and Equity Assessment
Evaluate models for discriminatory outcomes and fairness gaps.
12 chapters in this module
- Defining fairness in business context
- Protected attribute identification
- Disparate impact analysis
- Statistical parity testing
- Equal opportunity metrics
- Predictive parity validation
- Bias mitigation technique review
- Human-in-the-loop effectiveness
- Appeal and correction pathways
- Transparency of fairness claims
- Stakeholder perception audits
- Remediation tracking
Module 7. Explainability and Interpretability Review
Assess whether model decisions can be understood and challenged.
12 chapters in this module
- Right to explanation frameworks
- Local vs global interpretability
- SHAP, LIME, and surrogate models
- Feature importance validation
- Decision pathway mapping
- User-facing explanation adequacy
- Complex model documentation
- Trade-offs between accuracy and clarity
- Audit of explanation consistency
- Stakeholder comprehension testing
- Regulatory alignment on transparency
- Explainability tool validation
Module 8. Operational Resilience and Monitoring
Evaluate ongoing performance tracking and incident response readiness.
12 chapters in this module
- Performance degradation detection
- Real-time monitoring design
- Alert threshold appropriateness
- Incident logging and classification
- Model rollback procedures
- Failover mechanism validation
- Drift and concept shift protocols
- Human override functionality
- Load and stress testing records
- Change management controls
- Version control audit
- Post-deployment review cadence
Module 9. Compliance and Regulatory Alignment
Map AI practices to current and emerging compliance requirements.
12 chapters in this module
- GDPR and AI implications
- CCPA and automated decision-making
- Sector-specific rules (health, finance, education)
- Algorithmic accountability laws
- Recordkeeping expectations
- Third-party vendor compliance
- Audit rights in contracts
- Cross-border data flow impacts
- Regulatory sandbox participation
- Guidance from NIST, ISO, IEEE
- Internal policy alignment
- Compliance evidence packaging
Module 10. Control Design and Testing
Assess the design and operating effectiveness of AI-related controls.
12 chapters in this module
- Control objectives for AI systems
- Preventive vs detective controls
- Automated control validation
- Manual review point effectiveness
- Segregation of duties in AI workflows
- Access control audits
- Change approval workflows
- Logging and monitoring controls
- Control testing sampling methods
- Evidence collection protocols
- Control gap remediation
- Continuous control monitoring
Module 11. Reporting and Communication
Develop clear, actionable audit findings and recommendations.
12 chapters in this module
- Executive summary structuring
- Technical finding articulation
- Risk rating communication
- Visualizing model risk
- Recommendation specificity
- Action owner assignment
- Follow-up tracking systems
- Stakeholder feedback loops
- Board-level reporting formats
- Regulatory filing preparation
- Public disclosure considerations
- Lessons learned documentation
Module 12. Scaling AI Audit Capability
Build repeatable processes and team capacity for ongoing AI assurance.
12 chapters in this module
- Audit program roadmap development
- Capability maturity assessment
- Training plan design
- Tooling selection criteria
- Knowledge management setup
- Cross-functional collaboration
- Budgeting for AI audits
- Vendor and partner ecosystem
- Benchmarking against peers
- Continuous improvement cycle
- Success metric definition
- Leadership communication strategy
How this maps to your situation
- Auditing a new AI tool rollout
- Responding to regulatory inquiry on automated decisions
- Building internal AI governance framework
- Scaling audit team capacity for AI reviews
Before vs. after
Before
Uncertain how to approach AI audits with confidence, relying on ad-hoc methods and incomplete frameworks.
After
Equipped with a structured, repeatable process to lead AI model risk assessments and deliver actionable insights.
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 within 12 weeks with consistent pacing.
If nothing changes
Without a structured approach, audit teams risk inconsistent findings, diminished influence, and missed opportunities to shape responsible AI adoption in their organizations.
How this compares to the alternatives
Unlike academic courses focused on theory or enterprise frameworks requiring large teams, this program is tailored to mid-market realities, practical, implementation-focused, and designed for audit professionals with limited bandwidth and resources.
Frequently asked
Who is this course designed for?
Audit, risk, and compliance professionals in mid-market organizations who need to assess AI model risk but lack standardized tools or frameworks.
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 passing the final assessment.
$199 one-time. Approximately 3-4 hours per module, designed for completion within 12 weeks with consistent pacing..
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