A tailored course, built for your situation
Production-Grade Generative AI Policy Design for Senior Leaders
Build scalable, auditable AI governance frameworks that align with enterprise risk and innovation goals
$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.
Senior leaders are expected to lead on AI governance, but most lack structured, actionable frameworks to implement across teams and systems
The situation this course is for
Generative AI adoption is accelerating, yet policies remain ad hoc or theoretical. Leaders face pressure to demonstrate control without slowing innovation. Without a production-grade approach, organizations risk compliance gaps, operational friction, and loss of stakeholder trust, even as they invest heavily in AI capabilities.
Who this is for
Senior leaders in technology, compliance, risk, or strategy roles responsible for overseeing or enabling enterprise AI adoption
Who this is not for
Individual contributors focused only on model development, or practitioners seeking introductory AI ethics content
What you walk away with
- Design and deploy AI policies that are operationally enforceable, not just aspirational
- Align generative AI governance with existing risk, compliance, and IT frameworks
- Lead cross-functional initiatives with clear accountability and audit trails
- Anticipate regulatory expectations and prepare for external assessments
- Turn AI governance from a constraint into a strategic enabler
The 12 modules (with all 144 chapters)
Module 1. Foundations of Production-Grade AI Policy
Establish the core principles that differentiate operational AI governance from theoretical frameworks
12 chapters in this module
- Defining production-grade policy
- The evolution of AI governance standards
- Key stakeholders in AI policy execution
- Risk domains in generative AI
- Mapping policy to business impact
- Regulatory landscape overview
- Policy lifecycle stages
- Integration with enterprise architecture
- Measuring policy effectiveness
- Common implementation failures
- Scaling policy across business units
- Building executive sponsorship
Module 2. Governance Structure Design
Create organizational models that support consistent AI policy enforcement
12 chapters in this module
- Centralized vs decentralized governance
- AI governance office setup
- Cross-functional team roles
- Escalation pathways
- Decision rights framework
- Policy ownership models
- Steering committee operations
- Integration with ERM
- Reporting to executive leadership
- Board-level communication
- External advisor engagement
- Third-party oversight models
Module 3. Policy Architecture and Frameworks
Structure policies for clarity, consistency, and enforceability across technical and business domains
12 chapters in this module
- Layered policy design
- Principle-to-procedure mapping
- Policy version control
- Exception management
- Localization considerations
- Multi-jurisdictional alignment
- Policy taxonomy development
- Integration with code of conduct
- Automated policy distribution
- Policy accessibility standards
- Feedback loops for improvement
- Audit trail requirements
Module 4. Model Lifecycle Controls
Embed policy requirements into every stage of the AI development and deployment pipeline
12 chapters in this module
- Pre-development intake process
- Data sourcing compliance
- Bias assessment protocols
- Model documentation standards
- Validation and testing criteria
- Approval workflows
- Deployment checkpoints
- Monitoring in production
- Drift detection policies
- Incident response planning
- Decommissioning procedures
- Lessons learned integration
Module 5. Compliance and Regulatory Alignment
Anticipate and adapt to evolving legal and regulatory expectations
12 chapters in this module
- Global regulatory trends
- Sector-specific requirements
- Privacy by design integration
- Explainability standards
- Transparency obligations
- Recordkeeping mandates
- Cross-border data flow rules
- Licensing considerations
- Enforcement precedent analysis
- Regulator engagement strategy
- Self-assessment frameworks
- Pre-audit preparation
Module 6. Risk Assessment and Mitigation
Apply structured methods to identify, prioritize, and address AI-related risks
12 chapters in this module
- Risk taxonomy for generative AI
- Scenario-based threat modeling
- Impact likelihood matrices
- Control effectiveness scoring
- Residual risk evaluation
- Third-party risk integration
- Supply chain exposure
- Reputational risk mapping
- Financial exposure estimation
- Cybersecurity convergence
- Human rights impact assessment
- Crisis escalation planning
Module 7. Cross-Functional Alignment
Secure buy-in and coordination across legal, security, product, and engineering teams
12 chapters in this module
- Stakeholder influence mapping
- Change management for policy rollout
- Training program design
- Policy communication strategies
- Incentive alignment
- Conflict resolution protocols
- Feedback collection mechanisms
- Pilot program design
- Scaling from proof-of-concept
- Metrics for adoption success
- Executive dashboard design
- Continuous improvement cycles
Module 8. Technical Implementation Patterns
Translate policy into technical controls and system design requirements
12 chapters in this module
- Policy as code concepts
- API-level enforcement
- Access control integration
- Logging and monitoring specs
- Data retention rules
- Prompt engineering guardrails
- Output filtering mechanisms
- Authentication requirements
- Model watermarking
- Version tracking systems
- Audit log standards
- Automated compliance checks
Module 9. Audit and Assurance Readiness
Prepare for internal and external reviews with structured evidence collection
12 chapters in this module
- Internal audit coordination
- Evidence documentation standards
- Control testing procedures
- Third-party attestation
- SOC 2 and ISO alignment
- Regulatory inspection prep
- Gap assessment methodology
- Remediation tracking
- Audit response protocols
- Findings communication
- Corrective action plans
- Continuous monitoring integration
Module 10. Incident Response and Escalation
Build protocols for handling AI-related failures, breaches, or misuse
12 chapters in this module
- Incident classification framework
- Detection and reporting
- Initial assessment protocol
- Cross-team coordination
- Containment strategies
- Stakeholder notification
- Regulatory reporting
- Public communications
- Root cause analysis
- Remediation tracking
- Post-incident review
- Policy update process
Module 11. Stakeholder Communication Strategy
Tailor messaging for executives, regulators, customers, and employees
12 chapters in this module
- Executive briefing templates
- Board reporting cadence
- Regulator engagement plan
- Customer transparency
- Employee training content
- Vendor communication
- Media response guidelines
- Investor disclosure
- Ethics committee updates
- Public commitment statements
- Crisis communication planning
- Feedback integration
Module 12. Scaling and Continuous Improvement
Evolve AI governance to match organizational growth and technological change
12 chapters in this module
- Policy maturity model
- Benchmarking against peers
- Technology horizon scanning
- Feedback loop design
- Version upgrade planning
- Global expansion considerations
- Acquisition integration
- Resource planning
- Succession planning
- Knowledge transfer protocols
- Innovation sandbox governance
- Long-term roadmap development
How this maps to your situation
- Enterprise AI rollout planning
- Regulatory scrutiny preparation
- Cross-functional governance launch
- Post-incident policy rebuild
Before vs. after
Before
AI governance efforts are fragmented, reactive, and lack executive alignment
After
Policy frameworks are integrated, audit-ready, and actively support innovation at scale
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 completion over 12 weeks with flexible pacing.
If nothing changes
Organizations risk inconsistent enforcement, compliance gaps, and loss of stakeholder trust without structured, production-grade AI policy frameworks.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level risk frameworks, this program delivers implementation-grade policy architecture with enterprise-specific templates and real-world enforcement patterns.
Frequently asked
Who is this course designed for?
Senior leaders in technology, compliance, risk, or strategy roles responsible for overseeing enterprise AI adoption and governance.
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 3-4 hours per module, designed for completion over 12 weeks with flexible pacing..
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