A tailored course, built for your situation
Modern AI Model Risk Management for Compliance Officers
Implement AI governance with precision, confidence, and compliance-ready frameworks
$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.
AI adoption is accelerating, but compliance frameworks are struggling to keep pace with technical complexity and regulatory scrutiny.
The situation this course is for
Compliance officers face increasing pressure to assess AI systems without clear standards, consistent tooling, or implementation blueprints. Traditional risk models don’t map cleanly to dynamic, probabilistic AI behaviors, creating uncertainty in audits, reporting, and control design.
Who this is for
Compliance, risk, and governance professionals in technology, financial services, healthcare, and regulated industries who are responsible for overseeing AI deployments and ensuring regulatory alignment.
Who this is not for
This course is not for data scientists focused solely on model development, nor for executives seeking high-level overviews. It’s designed for practitioners who implement, audit, and govern AI systems.
What you walk away with
- Apply a structured framework to assess AI model risk across development, deployment, and monitoring phases
- Design compliance controls specific to generative AI and large language models
- Navigate evolving regulatory expectations from global bodies including the EU AI Act and U.S. federal guidance
- Integrate model risk documentation into existing governance workflows
- Build audit-ready evidence packages for internal and external reviewers
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Model Risk
Establish core definitions, risk categories, and governance principles specific to AI systems.
12 chapters in this module
- Defining AI model risk in regulated environments
- Differences between traditional and AI-driven risk profiles
- Key stakeholders in AI governance
- Regulatory drivers shaping current expectations
- Model lifecycle stages and risk touchpoints
- Common failure modes in production AI
- Ethical considerations in risk assessment
- Mapping AI risk to existing compliance frameworks
- Risk tolerance and thresholds for AI systems
- Documentation standards for model oversight
- Third-party AI vendor risk
- Case study: AI model incident response
Module 2. Regulatory Landscape Overview
Survey global standards, enforcement trends, and compliance expectations.
12 chapters in this module
- EU AI Act: compliance obligations by risk tier
- U.S. federal guidance on AI in financial services
- NIST AI Risk Management Framework alignment
- Sector-specific rules for healthcare and finance
- Cross-border data and model deployment issues
- Enforcement actions and supervisory trends
- Regulator expectations for model validation
- Transparency and explainability mandates
- Recordkeeping requirements for AI models
- Oversight responsibilities for boards and executives
- Future-looking regulatory proposals
- Benchmarking organizational readiness
Module 3. Model Risk Assessment Frameworks
Build and apply structured risk scoring systems for AI models.
12 chapters in this module
- Designing a risk taxonomy for AI systems
- Scoring model complexity and impact
- Data provenance and quality risk factors
- Bias and fairness assessment protocols
- Security vulnerabilities in AI pipelines
- Drift, degradation, and concept shift risks
- Human oversight requirements
- Scalability and performance thresholds
- Third-party model integration risks
- Supply chain and dependency risks
- Scenario-based risk testing
- Risk register design and maintenance
Module 4. Governance and Oversight Structures
Implement roles, committees, and escalation paths for AI compliance.
12 chapters in this module
- Establishing AI governance committees
- Defining roles: model owner, validator, reviewer
- Escalation protocols for model incidents
- Model inventory and registry design
- Change management for AI systems
- Audit planning and execution
- Internal reporting frameworks
- External disclosure strategies
- Vendor oversight and due diligence
- Model retirement and sunsetting processes
- Training and awareness programs
- Continuous monitoring integration
Module 5. Model Validation and Testing
Ensure models perform as intended and remain compliant over time.
12 chapters in this module
- Validation vs. verification: key distinctions
- Testing for accuracy and consistency
- Bias detection and mitigation techniques
- Stress testing AI under edge conditions
- Explainability testing methods
- Robustness against adversarial inputs
- Performance benchmarking over time
- Drift detection and response protocols
- Human-in-the-loop validation design
- Automated testing frameworks
- Validation documentation standards
- Third-party validation coordination
Module 6. Explainability and Transparency
Meet regulatory and stakeholder demands for model interpretability.
12 chapters in this module
- Regulatory expectations for explainability
- Technical methods for model interpretation
- Local vs. global explanations
- Saliency maps and feature importance
- Counterfactual explanations
- Model cards and fact sheets
- Transparency for end users
- Documentation for auditors
- Trade-offs between performance and explainability
- Explainability in generative AI
- User-facing disclosure design
- Audit trail requirements
Module 7. Bias Detection and Fairness Assurance
Identify, measure, and mitigate bias in AI systems.
12 chapters in this module
- Defining fairness in algorithmic decision-making
- Common sources of bias in training data
- Disparate impact analysis
- Fairness metrics and thresholds
- Pre-processing bias mitigation
- In-model fairness constraints
- Post-processing correction methods
- Bias testing across demographic groups
- Intersectional fairness assessment
- Bias reporting and disclosure
- Ongoing monitoring for drift in fairness
- Case study: bias incident investigation
Module 8. Data Quality and Provenance
Ensure data integrity throughout the AI lifecycle.
12 chapters in this module
- Data lineage and traceability
- Data quality dimensions for AI
- Training data representativeness
- Labeling accuracy and consistency
- Data drift detection methods
- Synthetic data risks and benefits
- Data privacy and anonymization
- Data access and retention policies
- Third-party data vendor oversight
- Data documentation standards
- Data versioning and audit trails
- Data poisoning and adversarial risks
Module 9. Security and Resilience
Protect AI systems from malicious and unintended threats.
12 chapters in this module
- Threat modeling for AI systems
- Adversarial attacks on models
- Model inversion and membership inference
- Secure model deployment environments
- Access controls for model endpoints
- Model integrity verification
- Incident response planning
- Red teaming AI systems
- Supply chain security for AI
- Monitoring for anomalous behavior
- Secure update and rollback procedures
- Resilience under stress conditions
Module 10. Audit and Documentation
Prepare for internal and external scrutiny with robust evidence.
12 chapters in this module
- Audit scope for AI systems
- Documenting model development lifecycle
- Model validation evidence packages
- Risk assessment documentation
- Governance committee minutes and records
- Compliance with internal policies
- External auditor expectations
- Regulatory examination readiness
- Version control and change logs
- Model performance reporting
- Incident documentation standards
- Audit trail automation
Module 11. Generative AI Specific Risks
Address unique challenges posed by large language models and generative systems.
12 chapters in this module
- Hallucination and factual inconsistency
- Copyright and intellectual property risks
- Prompt injection and manipulation
- Data leakage in generative outputs
- Use case appropriateness assessment
- Content moderation and filtering
- Brand risk in AI-generated content
- User identity and authentication
- Regulatory uncertainty in generative AI
- Monitoring for inappropriate content
- Human review thresholds
- Generative AI in customer-facing roles
Module 12. Implementation and Scaling
Operationalize AI risk management across the organization.
12 chapters in this module
- Change management for AI governance
- Training programs for compliance teams
- Tooling and platform selection
- Integration with GRC systems
- Scaling risk assessment across portfolios
- Continuous improvement cycles
- Benchmarking against peers
- Lessons from early adopters
- Building internal expertise
- Vendor ecosystem navigation
- Roadmap for maturity progression
- Sustaining executive engagement
How this maps to your situation
- Assessing AI risk in regulated environments
- Implementing governance structures for model oversight
- Validating models for accuracy, fairness, and compliance
- Preparing for audit and regulatory scrutiny
Before vs. after
Before
Uncertainty in assessing AI systems, inconsistent documentation, reactive compliance, and fragmented oversight.
After
Structured risk assessment, audit-ready documentation, proactive governance, and clear control frameworks for AI deployments.
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 self-paced learning with immediate applicability.
If nothing changes
Organizations that delay structured AI risk management may face regulatory scrutiny, reputational damage, and operational failures as AI systems scale without oversight.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level webinars, this program provides implementation-grade frameworks, detailed controls, and compliance-specific tooling tailored to regulated environments.
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, and governance professionals responsible for overseeing AI systems in regulated industries.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a certificate is issued upon finishing all modules and passing the final assessment.
$199 one-time. Approximately 3-4 hours per module, designed for self-paced learning with immediate applicability..
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