A tailored course, built for your situation
Audit-Tested Generative AI Policy Design for Senior Leaders
Implement-ready governance frameworks for next-generation AI integration
$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.
Knowing AI policy is critical, but lacking a tested, auditable framework to lead confidently
The situation this course is for
Senior leaders are expected to guide AI adoption, yet most frameworks are theoretical or reactive. Without an audit-tested approach, policies risk being dismissed during compliance reviews or failing under operational stress. The gap between intent and implementation leaves leadership exposed to governance failures, even with the best intentions.
Who this is for
Senior leaders in business and technology roles responsible for AI governance, risk, compliance, or strategic implementation, especially in regulated or scaling environments.
Who this is not for
Individual contributors without decision authority, entry-level staff, or teams focused only on AI model development without governance oversight.
What you walk away with
- Design generative AI policies that pass internal and external audits
- Align AI governance with existing compliance and risk management frameworks
- Lead cross-functional teams with confidence using structured decision pathways
- Anticipate regulatory expectations and build future-proof controls
- Deploy a living policy framework that evolves with AI capability and organizational needs
The 12 modules (with all 144 chapters)
Module 1. Foundations of Generative AI Governance
Establish core principles and leadership responsibilities in AI policy design.
12 chapters in this module
- Defining governance vs. oversight in AI
- Leadership’s role in ethical deployment
- Mapping AI use cases to policy scope
- Stakeholder identification and influence
- Regulatory landscape overview
- Risk categories in generative AI
- Policy maturity models
- Board-level engagement strategies
- Cross-departmental alignment
- Documentation standards
- Version control for policy artifacts
- Integrating AI governance into existing frameworks
Module 2. Audit Readiness and Compliance Mapping
Prepare policies to withstand formal review and regulatory scrutiny.
12 chapters in this module
- Understanding audit expectations
- Mapping controls to compliance requirements
- Documentation for traceability
- Evidence collection strategies
- Third-party assessment preparation
- Internal audit coordination
- Regulatory body expectations
- Control testing methodologies
- Gap analysis techniques
- Compliance dashboard design
- Audit communication protocols
- Post-audit improvement cycles
Module 3. Risk Assessment for Generative AI Systems
Identify, categorize, and prioritize risks specific to generative models.
12 chapters in this module
- Inherent vs. operational risk in AI
- Model hallucination and misinformation risk
- Data provenance and copyright concerns
- Bias and fairness evaluation
- Security vulnerabilities in AI pipelines
- Supply chain risks in model sourcing
- Reputational exposure scenarios
- Legal liability frameworks
- Risk scoring methodologies
- Scenario modeling for high-impact events
- Risk ownership assignment
- Escalation pathways for critical findings
Module 4. Control Framework Integration
Embed AI-specific controls into existing governance structures.
12 chapters in this module
- Adapting SOX, HIPAA, GDPR for AI
- Control mapping to NIST AI RMF
- Designing human-in-the-loop requirements
- Model input validation controls
- Output monitoring and logging
- Access governance for AI systems
- Change management for AI models
- Version control for prompts and pipelines
- Model drift detection controls
- Incident response integration
- Audit trail requirements
- Control testing frequency schedules
Module 5. Model Lifecycle Oversight
Govern AI systems from development through deployment and retirement.
12 chapters in this module
- Policy requirements for model development
- Vendor model procurement oversight
- Pre-deployment validation protocols
- Pilot program governance
- Go-live approval workflows
- Post-deployment monitoring mandates
- Model performance thresholds
- Drift detection and retraining triggers
- Decommissioning criteria
- Model lineage tracking
- Version rollback procedures
- Lifecycle documentation standards
Module 6. Stakeholder Alignment and Communication
Engage teams, leadership, and external partners with clarity and consistency.
12 chapters in this module
- Identifying key AI stakeholders
- Tailoring messaging by audience
- Board reporting frameworks
- Legal and compliance coordination
- IT and security team integration
- Legal counsel collaboration
- External auditor briefings
- Public disclosure guidelines
- Internal training requirements
- Feedback loop design
- Crisis communication planning
- Policy transparency strategies
Module 7. Ethical AI Principles and Application
Translate ethical guidelines into operational policy requirements.
12 chapters in this module
- Defining ethical AI for your organization
- Bias mitigation strategy design
- Fairness evaluation frameworks
- Transparency vs. confidentiality balance
- Explainability requirements
- Human dignity considerations
- Community impact assessment
- Ethics review board formation
- Whistleblower protections
- Ethical incident response
- Public trust metrics
- Ethics training integration
Module 8. Policy Implementation Roadmapping
Create phased, realistic plans for policy rollout and adoption.
12 chapters in this module
- Assessing organizational readiness
- Prioritizing high-risk use cases
- Resource allocation planning
- Cross-functional team structure
- Timeline development
- Milestone definition
- Success metric selection
- Change management strategy
- Training rollout planning
- Pilot program design
- Feedback integration mechanisms
- Scaling from pilot to enterprise
Module 9. Monitoring, Reporting, and Review
Establish ongoing oversight to ensure policy effectiveness and adaptation.
12 chapters in this module
- Key risk indicators for AI systems
- Dashboard design for leadership
- Automated alerting configurations
- Regular review meeting structure
- Incident reporting protocols
- Audit preparation cycles
- Regulatory change tracking
- Policy version management
- Stakeholder feedback collection
- Performance against benchmarks
- Lessons learned integration
- Continuous improvement frameworks
Module 10. Third-Party and Vendor Governance
Extend policy control to external AI providers and partners.
12 chapters in this module
- Vendor risk assessment criteria
- Contractual AI compliance terms
- Model transparency requirements
- Audit rights for third-party AI
- Performance SLAs for AI services
- Data handling in vendor systems
- Subcontractor oversight
- Incident notification obligations
- Exit strategy planning
- Vendor lock-in mitigation
- Due diligence checklists
- Ongoing vendor monitoring
Module 11. Crisis Response and Remediation
Prepare for and respond to AI-related incidents with confidence.
12 chapters in this module
- Defining AI incident types
- Incident escalation workflows
- Legal and regulatory reporting
- Public relations coordination
- Technical remediation steps
- Model rollback procedures
- Root cause analysis methods
- Corrective action planning
- Regulatory engagement protocols
- Post-incident review process
- Rebuilding stakeholder trust
- Policy update triggers
Module 12. Sustaining and Evolving AI Governance
Ensure long-term policy relevance and organizational resilience.
12 chapters in this module
- Governance maturity measurement
- Adapting to new AI capabilities
- Regulatory change anticipation
- Policy version control
- Knowledge transfer strategies
- Leadership onboarding
- Succession planning for AI oversight
- Lessons learned documentation
- Benchmarking against peers
- Innovation enablement balance
- Future-proofing policy design
- Closing the governance loop
How this maps to your situation
- Leading AI governance in a regulated industry
- Responding to audit findings with updated policy
- Scaling AI use cases with consistent oversight
- Building board-ready AI governance reports
Before vs. after
Before
Uncertain about how to structure AI policies that meet audit standards and leadership expectations
After
Confidently lead the design and rollout of audit-tested, implementation-ready AI governance frameworks
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 3-5 hours per module, designed for self-paced learning with immediate applicability.
If nothing changes
Without a structured, audit-ready approach, AI policies risk being superficial or reactive, leading to compliance gaps, leadership challenges during review cycles, and missed opportunities to shape responsible AI adoption at scale.
How this compares to the alternatives
Unlike generic AI ethics guides or high-level overviews, this course delivers implementation-grade policy frameworks with audit-specific controls, real-world templates, and a step-by-step playbook, making it the most practical resource for senior leaders accountable for AI governance.
Frequently asked
Who is this course designed for?
Senior leaders in business and technology roles responsible for AI governance, risk, compliance, or strategic implementation, especially in regulated or scaling environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or strategic?
It is strategically focused for leadership, with implementation-grade detail, balancing governance principles with actionable frameworks, not code or engineering specifics.
$199 one-time. Approximately 3-5 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