A tailored course, built for your situation
AI Governance for Risk and Compliance Teams
Implement responsible AI frameworks with confidence, clarity, and control
$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.
You’re expected to govern AI systems you didn’t build, with no clear framework, rising regulatory stakes, and little time to get it right.
The situation this course is for
AI adoption is accelerating, but compliance teams are being asked to assess models they don’t understand, using standards that don’t yet exist. Audits are failing, board questions are increasing, and the risk of reputational or regulatory penalties is real. Without a structured approach, AI governance becomes reactive, fragmented, and high-pressure.
Who this is for
Risk, compliance, or governance professional in a mid-to-large organization adopting AI in finance, operations, or data systems
Who this is not for
Developers building AI models, executives seeking strategy decks, or teams without active AI deployment initiatives
What you walk away with
- Deploy a compliant, auditable AI governance framework in 90 days
- Align AI controls with ISO 38507, NIST AI RMF, and EU AI Act requirements
- Establish model inventory, risk tiering, and monitoring protocols
- Integrate AI assurance into existing audit and risk workflows
- Produce board-ready reports on AI risk posture
The 12 modules (with all 144 chapters)
Module 1. AI Governance Foundations
Establish the core principles of AI governance, including ethical boundaries, regulatory scope, and risk categorization frameworks. Understand the difference between AI assurance and traditional IT audit. Learn how to classify AI systems by impact level and compliance criticality. This module sets the baseline for structured governance across use cases.
12 chapters in this module
- What is AI governance
- Regulatory landscape overview
- Risk-based classification
- Ethical boundaries definition
- AI vs traditional systems
- Governance team roles
- Stakeholder alignment map
- Compliance threshold setting
- Use case prioritization
- Audit scope definition
- Policy foundation drafting
- Governance maturity model
Module 2. Model Risk Management
Adapt financial model risk concepts to AI systems. Learn how to document model purpose, inputs, limitations, and performance thresholds. Build model risk registers and define escalation paths. This module focuses on documentation standards and control testing for both internal and third-party models.
12 chapters in this module
- Model inventory creation
- Input data validation
- Performance benchmarking
- Model documentation standards
- Risk register setup
- Escalation protocols
- Third-party model oversight
- Model drift detection
- Version control tracking
- Model deprecation process
- Audit trail requirements
- Model validation cycles
Module 3. Compliance Alignment
Map AI activities to existing regulations like GDPR, SOX, and new frameworks like the EU AI Act. Identify high-risk categories and mandatory documentation requirements. This module helps you align AI governance with legal obligations and avoid regulatory penalties.
12 chapters in this module
- GDPR and AI linkage
- SOX implications
- EU AI Act compliance
- High-risk use cases
- Transparency obligations
- Data subject rights
- Recordkeeping mandates
- Third-country transfers
- Algorithmic impact assessment
- Conformity assessment process
- Compliance gap analysis
- Regulatory reporting formats
Module 4. Explainability and Auditability
Ensure AI decisions can be explained and audited. Learn techniques for model interpretability, output justification, and audit trail generation. This module provides tools to answer 'Why did the model decide that?' in a way auditors and regulators accept.
12 chapters in this module
- Explainability methods overview
- Local vs global interpretability
- SHAP values application
- LIME for model insight
- Audit trail design
- Decision logging standards
- Output justification
- Human oversight integration
- Error case documentation
- Bias detection protocols
- Model confidence reporting
- Audit readiness checklist
Module 5. Data Governance for AI
AI systems depend on data quality, lineage, and access controls. This module covers data provenance tracking, bias mitigation in training sets, and data quality monitoring. Learn how to ensure inputs meet governance standards.
12 chapters in this module
- Data provenance mapping
- Training set validation
- Bias in data detection
- Data quality metrics
- Access control policies
- Data lifecycle management
- Anonymization techniques
- Data drift monitoring
- Labeling accuracy checks
- Data versioning
- Metadata documentation
- Data audit trail
Module 6. AI Risk Assessment
Conduct structured AI risk assessments using repeatable frameworks. Learn to evaluate fairness, robustness, privacy, and security risks. This module provides a standardized assessment template for all AI initiatives.
12 chapters in this module
- Risk assessment framework
- Fairness evaluation
- Robustness testing
- Privacy risk scoring
- Security threat modeling
- Adversarial attack simulation
- Failure mode analysis
- Impact likelihood matrix
- Risk treatment options
- Risk acceptance criteria
- Third-party risk review
- Ongoing monitoring plan
Module 7. Monitoring and Ongoing Assurance
Set up continuous monitoring for AI systems in production. Learn how to track performance decay, detect bias shifts, and log decision patterns. This module ensures governance doesn’t end at deployment.
12 chapters in this module
- Performance decay tracking
- Bias shift detection
- Decision pattern logging
- Model retraining triggers
- Alert threshold setting
- Anomaly detection
- Human-in-the-loop design
- Feedback loop integration
- Model monitoring tools
- Incident response plan
- Escalation workflows
- Audit readiness updates
Module 8. AI Ethics and Accountability
Establish ethical guidelines for AI use and assign clear accountability. Learn how to create ethics review boards, define red lines, and enforce accountability across teams. This module ensures responsible innovation.
12 chapters in this module
- Ethics framework design
- Red line definition
- Accountability mapping
- Ethics review board setup
- Use case approval process
- Whistleblower mechanisms
- Ethical impact assessment
- Stakeholder consultation
- Public trust metrics
- Reputational risk review
- Ethics training rollout
- Ethics audit process
Module 9. Third-Party AI Oversight
Govern AI models and services from external vendors. Learn how to assess vendor compliance, review model cards, and enforce contractual obligations. This module ensures third-party AI meets internal standards.
12 chapters in this module
- Vendor due diligence
- Model card review
- Contractual obligations
- Compliance verification
- Audit rights negotiation
- Performance SLAs
- Data handling terms
- Exit strategy planning
- Vendor risk scoring
- Ongoing monitoring
- Incident response coordination
- Vendor offboarding
Module 10. Board and Executive Reporting
Communicate AI risk and governance posture to leadership. Learn how to create concise, actionable reports for boards and executives. This module ensures governance visibility at the highest levels.
12 chapters in this module
- Board reporting structure
- Risk dashboard design
- Key risk indicators
- Executive summary format
- Risk appetite alignment
- Incident communication
- Budget justification
- Strategic alignment
- Regulatory update summary
- Governance KPIs
- Audit finding reporting
- Future risk forecasting
Module 11. Incident Response for AI
Prepare for AI-related incidents like biased outputs, security breaches, or model failures. Learn how to respond, contain, and report issues effectively. This module ensures resilience.
12 chapters in this module
- Incident classification
- Response team roles
- Containment procedures
- Root cause analysis
- Bias incident handling
- Security breach response
- Model rollback process
- Regulatory notification
- Stakeholder communication
- Post-mortem review
- Corrective action tracking
- Recovery validation
Module 12. Scaling AI Governance
Expand governance across multiple teams, departments, or geographies. Learn how to standardize practices, train teams, and integrate with enterprise risk systems. This module ensures long-term sustainability.
12 chapters in this module
- Governance standardization
- Team training rollout
- Centralized oversight
- Local adaptation rules
- Cross-functional alignment
- Knowledge sharing
- Tooling integration
- Maturity assessment
- Continuous improvement
- Feedback integration
- Policy update cycle
- Enterprise risk integration
How this maps to your situation
- AI adoption in finance and data systems
- Regulatory scrutiny on algorithmic decisions
- Need for audit-ready documentation
- Executive demand for governance clarity
Before vs. after
Before
Overwhelmed by AI initiatives without clear governance, facing auditor questions, and scrambling to define controls after deployment.
After
Confidently governing AI systems with structured frameworks, clear documentation, and board-ready reporting.
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 busy professionals to complete at their own pace over 8, 12 weeks.
If nothing changes
Without a formal AI governance approach, organizations face regulatory fines, reputational damage, and loss of stakeholder trust, especially as AI use grows in sensitive areas like finance and data processing.
How this compares to the alternatives
Unlike generic AI ethics courses or technical model interpretability guides, this program is built specifically for compliance and risk teams who need actionable, audit-ready frameworks, not theory.
Frequently asked
Who is this course for?
Risk, compliance, and governance professionals responsible for overseeing AI systems in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this technical or conceptual?
It’s practical and process-focused, designed for professionals who govern AI, not build it.
$199 one-time. Approximately 3 hours per module, designed for busy professionals to complete at their own pace over 8, 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