A tailored course, built for your situation
AI Governance for Risk and Compliance Teams
Govern generative AI adoption 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.
Your organization is already using AI , but without formal governance, you're one audit away from a compliance breach.
The situation this course is for
Teams are deploying generative AI tools independently, creating blind spots in data security, regulatory compliance, and ethical use. Governance teams are expected to respond, but lack practical frameworks tailored to fast-moving AI risks. Without clear policies, oversight mechanisms, and alignment with existing controls, organizations face reputational damage, regulatory penalties, and operational drift.
Who this is for
Risk officers, compliance leads, and governance professionals in mid-to-large organizations adopting AI at scale
Who this is not for
Individual contributors without policy influence, technical AI developers, or teams focused only on model accuracy or infrastructure
What you walk away with
- Build an AI governance framework aligned with NIST, ISO, and sector-specific regulations
- Implement audit-ready controls for AI usage across departments
- Map AI risk to existing compliance frameworks like GDPR, HIPAA, or SOX
- Establish monitoring protocols for ethical AI use and bias mitigation
- Create a defensible AI oversight function that scales with adoption
The 12 modules (with all 144 chapters)
Module 1. The State of AI in Enterprise Today
Understand how generative AI is being adopted across functions and the immediate risks this creates for compliance and governance teams. Review real-world incidents, regulatory responses, and the expanding attack surface introduced by unapproved AI tools.
12 chapters in this module
- AI adoption trends in 2025
- Common use cases by department
- Shadow AI in the wild
- Regulatory scrutiny rising
- High-profile AI failures
- Sector-specific exposure
- Vendor AI vs in-house models
- Employee-driven AI use
- Data leakage risks
- Model hallucination impacts
- Compliance blind spots
- The cost of inaction
Module 2. Defining AI Governance
Establish a working definition of AI governance that aligns with organizational risk appetite. Learn how to distinguish between AI ethics, safety, and compliance, and build foundational principles for oversight.
12 chapters in this module
- What governance means for AI
- Ethics vs compliance
- Risk-based framing
- Governance vs oversight
- Core principles defined
- Accountability frameworks
- Roles and responsibilities
- Policy ownership
- Cross-functional alignment
- Enforcement mechanisms
- Escalation paths
- Documentation standards
Module 3. Mapping AI to Regulatory Frameworks
Align AI activities with existing compliance requirements including GDPR, HIPAA, SOX, and emerging AI-specific regulations. Identify where AI use creates new obligations or amplifies existing risks.
12 chapters in this module
- GDPR and AI profiling
- HIPAA in AI workflows
- SOX controls and AI
- CCPA implications
- NIST AI RMF alignment
- EU AI Act tiers
- Sector-specific rules
- Cross-border data flows
- Audit trail requirements
- Model transparency rules
- Bias and fairness laws
- Recordkeeping mandates
Module 4. AI Risk Assessment Methodology
Develop a repeatable process for evaluating AI risk across projects. Learn to score models based on impact, data sensitivity, and autonomy level, and integrate assessments into existing risk management workflows.
12 chapters in this module
- Risk scoring framework
- Impact level definitions
- Data sensitivity tiers
- Autonomy levels
- Third-party model risks
- Fine-tuning considerations
- Model lifecycle stages
- Use case categorization
- Risk threshold setting
- Approval workflows
- Documentation templates
- Review cadence planning
Module 5. Policy Design for AI Use
Create enforceable AI usage policies that balance innovation with control. Cover acceptable use, employee responsibilities, vendor oversight, and escalation procedures.
12 chapters in this module
- Acceptable use policy
- Employee responsibilities
- Prohibited use cases
- Approved tools list
- Vendor AI governance
- Model documentation
- Data handling rules
- Security requirements
- Incident reporting
- Policy enforcement
- Training obligations
- Review and update cycle
Module 6. AI Oversight and Monitoring
Implement technical and procedural controls to monitor AI use across the organization. Learn to detect unauthorized tools, track model performance, and ensure ongoing compliance.
12 chapters in this module
- Monitoring scope definition
- Tool discovery methods
- Network traffic analysis
- Cloud usage tracking
- Model performance logs
- Bias detection tools
- Human review triggers
- Anomaly detection
- Compliance dashboards
- Audit logging
- Alerting protocols
- Remediation workflows
Module 7. AI Incident Response
Prepare for AI-related incidents including hallucinations, bias exposure, data leaks, and regulatory inquiries. Build a response plan that integrates with existing incident management.
12 chapters in this module
- AI incident types
- Hallucination response
- Bias exposure protocol
- Data leak containment
- Regulatory inquiry prep
- Legal hold procedures
- Stakeholder comms
- Root cause analysis
- Remediation tracking
- Escalation paths
- Post-mortem process
- Reporting templates
Module 8. Vendor and Third-Party AI Management
Assess and govern third-party AI tools and APIs. Learn to evaluate vendor compliance, negotiate SLAs, and maintain oversight of external models.
12 chapters in this module
- Vendor risk tiers
- Due diligence checklist
- AI-specific SLAs
- Model transparency
- Data ownership terms
- Audit rights
- Subprocessor tracking
- Compliance certifications
- Contractual safeguards
- Ongoing monitoring
- Exit strategies
- Vendor offboarding
Module 9. AI Ethics and Bias Mitigation
Address ethical concerns and bias in AI systems. Implement processes to detect, document, and reduce unfair outcomes in automated decisions.
12 chapters in this module
- Ethical principles
- Bias types defined
- Fairness metrics
- Disparate impact
- Model fairness testing
- Bias detection tools
- Human review process
- Appeals mechanism
- Transparency reporting
- Stakeholder feedback
- Bias remediation
- Ethics review board
Module 10. AI Audits and Assurance
Prepare for internal and external audits of AI systems. Build documentation packages, conduct self-assessments, and demonstrate compliance to auditors.
12 chapters in this module
- Audit readiness checklist
- Control mapping
- Evidence collection
- Self-assessment process
- Third-party audits
- Regulatory exams
- Findings response
- Compliance dashboards
- Audit trail setup
- Documentation standards
- Remediation tracking
- Continuous assurance
Module 11. Building the AI Governance Function
Establish a dedicated AI governance role or team. Define scope, secure budget, and integrate with existing compliance and risk functions.
12 chapters in this module
- Governance team scope
- Staffing models
- Budget justification
- Stakeholder alignment
- Cross-functional teams
- Reporting structure
- KPIs and metrics
- Maturity model
- Executive reporting
- Training programs
- External partnerships
- Continuous improvement
Module 12. Scaling AI Governance
Adapt governance frameworks as AI adoption grows. Learn to automate controls, expand oversight, and future-proof policies for emerging AI capabilities.
12 chapters in this module
- Scaling challenges
- Automation opportunities
- Policy versioning
- AI registry setup
- Model inventory
- Lifecycle management
- Continuous monitoring
- Feedback loops
- Emerging tech watch
- GenAI evolution
- Autonomous systems
- Future governance needs
How this maps to your situation
- Your organization adopts AI tools without governance
- Compliance team lacks AI-specific controls
- Regulators increase scrutiny on AI use
- Leadership demands oversight framework
Before vs. after
Before
AI tools are in use across departments with no oversight, creating compliance blind spots and audit risk.
After
You have a live AI governance framework with policies, monitoring, and incident response , audit-ready and leadership-approved.
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 2 hours per module, designed to be completed at your pace over 6, 8 weeks.
If nothing changes
Without governance, AI use leads to undetected data leaks, regulatory penalties, reputational damage, and loss of stakeholder trust , all avoidable with structured oversight.
How this compares to the alternatives
Unlike generic AI ethics courses or technical model audits, this program delivers actionable governance playbooks specifically for compliance and risk professionals , not data scientists.
Frequently asked
Who is this course for?
Risk, compliance, and governance leaders responsible for overseeing AI adoption in their organizations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does it include templates?
Yes , every module includes downloadable templates and real-world examples.
$199 one-time. Approximately 2 hours per module, designed to be completed at your pace over 6, 8 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