A tailored course, built for your situation
AI Governance for Compliance and Risk Leaders
Master the framework to govern AI deployments with confidence, compliance, 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 accountable for risk, but AI systems make decisions in ways no one can explain , and regulators are watching.
The situation this course is for
AI is being adopted faster than policies can keep up. Compliance teams are left reacting to deployments they didn’t approve, with no audit trail, no documentation, and no enforcement mechanism. When something goes wrong, the blame lands on governance. You need a way to get ahead , not just catch up.
Who this is for
Mid-career compliance, risk, or governance professionals stepping into oversight of AI and automated decision systems.
Who this is not for
Data scientists building models, executives wanting high-level summaries, or teams seeking technical AI implementation guides.
What you walk away with
- Audit AI systems even without technical expertise
- Map AI risk to existing compliance frameworks
- Enforce documentation and model transparency
- Build incident response protocols for AI failures
- Position governance as an enabler, not a blocker
The 12 modules (with all 144 chapters)
Module 1. The AI Governance Imperative
Understand why traditional compliance frameworks fail with AI systems. Explore real incidents where lack of oversight led to regulatory penalties and reputational damage. Learn how AI differs from legacy software in risk profile and audit complexity. Establish the business case for proactive governance.
12 chapters in this module
- Why AI breaks compliance
- Regulatory pressure points
- High-profile AI failures
- Accountability gaps
- The speed-risk imbalance
- Emerging enforcement trends
- Internal adoption patterns
- Shadow AI in departments
- Vendor model risks
- Liability exposure paths
- Reputational impact cases
- From reactive to proactive
Module 2. Mapping AI to Compliance Frameworks
Translate AI risks into existing governance structures like SOC 2, ISO 27001, HIPAA, or GLBA. Learn how to extend current controls to cover model behavior, data drift, and decision explainability. Use familiar frameworks as anchors for new AI-specific policies.
12 chapters in this module
- SOC 2 and AI systems
- ISO 27001 extensions
- HIPAA in AI contexts
- GLBA compliance mapping
- Privacy law alignment
- GDPR and automated decisions
- CCPA implications
- Audit scope adjustments
- Control overlap analysis
- Gap identification method
- Risk tiering models
- Compliance mapping matrix
Module 3. AI Risk Taxonomy
Build a standardized classification system for AI risks across your organization. Categorize by impact level, decision criticality, data sensitivity, and automation degree. Use this taxonomy to prioritize audits, allocate resources, and report up the chain.
12 chapters in this module
- Decision criticality levels
- Data sensitivity tiers
- Automation thresholds
- Bias risk indicators
- Model opacity scoring
- Third-party dependency
- Output impact assessment
- Human-in-the-loop needs
- Feedback loop risks
- Drift detection triggers
- Incident severity bands
- Risk classification matrix
Module 4. Model Documentation Standards
Enforce consistent documentation across teams using AI. Define minimum viable documentation for models in production. Create templates for model cards, data lineage, and performance thresholds. Implement review gates before deployment.
12 chapters in this module
- Model card essentials
- Data source tracking
- Training data provenance
- Performance benchmarks
- Version control rules
- Change approval process
- Retraining schedules
- Monitoring thresholds
- Stakeholder sign-offs
- Documentation audit trail
- Enforcement escalation
- Template rollout plan
Module 5. AI Audit Methodology
Develop a repeatable process to audit AI systems without needing data science expertise. Use control-based sampling, input-output tracing, and bias testing. Generate audit reports that speak to both technical and executive audiences.
12 chapters in this module
- Audit scope definition
- Control-based sampling
- Input-output tracing
- Bias testing protocols
- Model card review
- Data drift checks
- Performance validation
- Compliance alignment
- Findings categorization
- Reporting templates
- Remediation tracking
- Audit cycle planning
Module 6. Explainability and Transparency
Demand clarity from data science teams using structured frameworks. Apply techniques like SHAP, LIME, or counterfactuals even if you don’t build models. Translate technical outputs into governance insights.
12 chapters in this module
- Explainability definitions
- SHAP for non-experts
- LIME interpretation
- Counterfactual testing
- Feature importance
- Model confidence levels
- Uncertainty reporting
- Decision logs
- Human review triggers
- Transparency scorecard
- Stakeholder communication
- Vendor explainability demands
Module 7. Bias Detection and Mitigation
Identify hidden biases in AI systems using audit trails and outcome analysis. Apply statistical tests to detect disproportionate impacts. Build escalation paths for biased outcomes and define correction protocols.
12 chapters in this module
- Bias definition types
- Disparate impact test
- Statistical parity check
- Predictive equality
- Conditional use metrics
- Outcome monitoring
- Demographic analysis
- Error rate comparison
- Bias correction steps
- Appeal process design
- Third-party audits
- Bias reporting template
Module 8. Incident Response for AI Failures
Prepare for AI system failures with structured response playbooks. Define escalation paths, communication protocols, and remediation steps. Conduct tabletop exercises to test readiness.
12 chapters in this module
- Failure mode identification
- Escalation pathways
- Communication plan
- Remediation checklist
- Model rollback steps
- Stakeholder notification
- Regulatory reporting
- Post-mortem process
- Tabletop exercise design
- Response team roles
- Legal exposure review
- Public statement prep
Module 9. Vendor AI Oversight
Extend governance to third-party AI tools and APIs. Define contractual requirements, audit rights, and performance guarantees. Monitor vendor models for compliance drift.
12 chapters in this module
- Vendor contract clauses
- Audit rights negotiation
- Performance SLAs
- Data handling terms
- Model change notice
- Compliance certification
- Third-party assessments
- API monitoring
- Subprocessor tracking
- Exit strategy planning
- Vendor scorecard
- Oversight escalation
Module 10. AI Policy Development
Write enforceable AI policies tailored to your organization’s risk appetite. Align with legal, security, and ethics standards. Roll out with training and accountability measures.
12 chapters in this module
- Policy scope definition
- Risk appetite alignment
- Approval workflows
- Enforcement mechanisms
- Training requirements
- Accountability mapping
- Monitoring frequency
- Review cycles
- Exception process
- Policy communication
- Adoption tracking
- Version control
Module 11. Stakeholder Communication
Bridge the gap between technical teams and executives. Translate AI risks into business terms. Build trust through transparency and structured reporting.
12 chapters in this module
- Executive summary format
- Technical to business
- Risk communication
- Board reporting
- Legal team coordination
- IT alignment
- Public messaging
- Crisis comms prep
- Internal training
- Feedback collection
- Stakeholder map
- Communication calendar
Module 12. Scaling Governance Across AI Use Cases
Expand governance from pilot projects to enterprise-wide AI adoption. Build centralized oversight with decentralized execution. Automate monitoring and reporting at scale.
12 chapters in this module
- Centralized governance model
- Decentralized execution
- AI inventory tracking
- Automated monitoring
- Dashboard reporting
- Resource allocation
- Cross-functional teams
- Maturity assessment
- Continuous improvement
- Scaling playbook
- Budget planning
- Future-proofing
How this maps to your situation
- AI systems deployed without oversight
- Regulatory scrutiny increasing
- Internal teams using unapproved tools
- Need for audit-ready documentation
Before vs. after
Before
Overwhelmed by fast-moving AI projects with no governance, unclear accountability, and rising regulatory risk.
After
Confidently leading oversight with structured frameworks, audit-ready documentation, and enforcement playbooks.
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 to be completed at your pace over 6-8 weeks.
If nothing changes
Without structured AI governance, your organization faces undetected bias, regulatory fines, reputational damage, and loss of stakeholder trust , all stemming from systems you can't explain or control.
How this compares to the alternatives
Unlike generic compliance courses or technical AI trainings, this program is built specifically for risk and governance professionals who need to lead oversight without becoming data scientists.
Frequently asked
Do I need a technical background to benefit from this course?
No. The course is designed for compliance and risk professionals without data science expertise.
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 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