A tailored course, built for your situation
Production-Grade Generative AI Policy Design for Established Enterprises
A 12-module implementation framework for enterprise governance, risk, and compliance leaders
$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.
Policies that sound good but fail under audit or scaling pressure
The situation this course is for
Teams invest heavily in AI ethics and governance principles, only to find them unenforceable, misaligned across departments, or disconnected from technical implementation. This leads to rework, compliance gaps, and eroded stakeholder trust when initiatives scale.
Who this is for
Mid-to-senior level professionals in enterprise governance, risk, compliance, data policy, or technology strategy leading or contributing to generative AI oversight in organizations with existing regulatory, operational, or scale constraints.
Who this is not for
Individual contributors focused only on research AI, startups without formal compliance structures, or teams still exploring basic AI use cases without governance mandates.
What you walk away with
- Design enforceable, auditable generative AI policies aligned with technical and operational realities
- Map controls to regulatory expectations and internal risk thresholds
- Coordinate policy rollout across legal, security, engineering, and business units
- Integrate policy requirements into development lifecycle and vendor management processes
- Build board-ready documentation and escalation protocols for AI governance
The 12 modules (with all 144 chapters)
Module 1. Foundations of Enterprise AI Governance
Establish core definitions, governance models, and stakeholder alignment strategies for generative AI.
12 chapters in this module
- Defining production-grade AI policy
- Governance vs. compliance vs. risk management
- Identifying internal policy stakeholders
- Aligning with existing enterprise frameworks
- Lifecycle overview of policy development
- Regulatory landscape mapping
- Internal audit expectations
- Policy ownership models
- Cross-functional coordination mechanisms
- Documentation standards
- Version control and change management
- Baseline assessment toolkit
Module 2. Risk Classification for Generative AI Systems
Develop a consistent methodology for categorizing AI risk across impact dimensions.
12 chapters in this module
- High-impact vs. low-risk use cases
- Harm typologies in generative AI
- Data sensitivity and model transparency
- Automated decision-making thresholds
- Third-party model risk assessment
- User interaction risk levels
- Scalability and drift considerations
- Incident severity tiering
- Risk scoring matrix design
- Validation with legal and compliance
- Risk re-evaluation triggers
- Risk classification playbook
Module 3. Control Framework Design and Mapping
Translate risk categories into specific, actionable controls across technical and procedural domains.
12 chapters in this module
- Control types: preventive, detective, corrective
- Mapping controls to risk tiers
- Technical controls for model inputs and outputs
- Human-in-the-loop requirements
- Access control and authentication rules
- Logging and monitoring mandates
- Bias detection and mitigation protocols
- Content moderation workflows
- Vendor control expectations
- Control ownership assignment
- Control testing procedures
- Control mapping template
Module 4. Policy Development Lifecycle
Implement a repeatable process for drafting, reviewing, approving, and updating AI policies.
12 chapters in this module
- Stakeholder intake process
- Drafting policy language for clarity
- Versioning and change tracking
- Legal review coordination
- Engineering feasibility assessment
- Business unit feedback loops
- Approval workflows and sign-offs
- Publication and accessibility standards
- Training and awareness rollout
- Feedback collection mechanisms
- Scheduled review cycles
- Lifecycle automation options
Module 5. Cross-Functional Alignment Strategies
Enable collaboration between legal, security, engineering, HR, and business units on AI policy execution.
12 chapters in this module
- Identifying alignment friction points
- Creating joint working groups
- Shared vocabulary development
- Conflict resolution protocols
- Escalation pathways for disputes
- Joint training sessions design
- Policy ambassador programs
- Feedback integration methods
- Progress tracking dashboards
- Incentive alignment across teams
- Leadership communication plans
- Alignment scorecard template
Module 6. Technical Integration of Policy Requirements
Embed policy mandates into development practices, MLOps, and platform configurations.
12 chapters in this module
- Policy requirements in user stories
- Pre-deployment compliance checks
- Model registration and inventory
- Prompt engineering guardrails
- Output filtering and redaction
- Model monitoring for drift and misuse
- API-level enforcement points
- Audit logging standards
- Security scanning integration
- DevSecOps pipeline alignment
- Break-glass override protocols
- Technical enforcement checklist
Module 7. Vendor and Third-Party Management
Extend policy requirements to external AI providers and integrated tools.
12 chapters in this module
- Third-party risk assessment criteria
- Contractual clauses for AI use
- Vendor audit rights and transparency
- Subprocessor oversight
- Model provenance tracking
- Data handling compliance verification
- Incident response coordination
- Performance and bias monitoring
- Exit strategy and data portability
- Due diligence checklist
- Ongoing monitoring plan
- Vendor management playbook
Module 8. Incident Response and Escalation Planning
Prepare for and respond to policy violations, model failures, or unintended harms.
12 chapters in this module
- Defining AI incidents and near-misses
- Reporting channels and intake process
- Triage and severity classification
- Cross-functional incident team
- Containment and mitigation actions
- Root cause analysis methods
- Stakeholder communication plan
- Regulatory reporting obligations
- Public disclosure protocols
- Post-incident review process
- Preventive action tracking
- Incident response playbook
Module 9. Audit and Compliance Readiness
Structure policies and evidence to meet internal and external audit requirements.
12 chapters in this module
- Audit scope definition
- Evidence collection standards
- Control testing documentation
- Regulatory mapping (sector-specific)
- Internal audit coordination
- External auditor engagement
- Gap assessment process
- Remediation tracking
- Compliance dashboard design
- Policy exception management
- Audit trail preservation
- Compliance readiness checklist
Module 10. Training and Awareness Programs
Design role-specific training to ensure policy understanding and adherence.
12 chapters in this module
- Audience segmentation for training
- Policy literacy assessment
- Role-based training content
- Delivery formats and platforms
- Interactive scenario design
- Comprehension testing
- Manager enablement resources
- Ongoing reinforcement tactics
- Training completion tracking
- Feedback and improvement loop
- Awareness campaign calendar
- Training program template
Module 11. Metrics, Monitoring, and Continuous Improvement
Establish KPIs and feedback systems to measure policy effectiveness and evolve over time.
12 chapters in this module
- Policy adherence metrics
- Incident frequency and severity trends
- Control effectiveness measurement
- Stakeholder satisfaction surveys
- Audit finding trends
- Training completion rates
- Policy update velocity
- Benchmarking against peers
- Feedback integration process
- Quarterly governance reviews
- Improvement backlog management
- Performance dashboard template
Module 12. Scaling and Sustaining AI Governance
Plan for long-term governance maturity as AI adoption expands across the enterprise.
12 chapters in this module
- Governance maturity model
- Center of excellence design
- Resource planning and staffing
- Budgeting for ongoing governance
- Executive sponsorship models
- Board reporting cadence
- Integration with enterprise strategy
- Change management for scaling
- Knowledge retention strategies
- External engagement and thought leadership
- Succession planning
- Sustainability roadmap template
How this maps to your situation
- You're launching your first enterprise-wide AI policy and need structure
- You've drafted principles but struggle with enforcement and scalability
- You face audit pressure and need defensible, documented controls
- You're coordinating across silos and need alignment frameworks
Before vs. after
Before
Disjointed AI governance efforts, reactive responses to risk, and policies that lack technical grounding or audit readiness.
After
A structured, enforceable, and scalable policy framework that aligns legal, technical, and business stakeholders and stands up to scrutiny.
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 45, 60 hours total, designed for flexible, self-paced completion over 6, 8 weeks.
If nothing changes
Without a production-grade approach, organizations risk inconsistent enforcement, compliance failures during audits, operational friction during scaling, and reputational harm from preventable incidents.
How this compares to the alternatives
Unlike high-level AI ethics guides or academic overviews, this course delivers implementable structure, control mappings, and enterprise-grade templates designed for real-world deployment in regulated environments.
Frequently asked
Who is this course designed for?
Mid-to-senior level professionals in governance, risk, compliance, data policy, or technology leadership roles in established organizations implementing or scaling generative AI systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a digital certificate of completion is available after finishing all modules and passing the final assessment.
$199 one-time. Approximately 45, 60 hours total, designed for flexible, self-paced completion 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