A tailored course, built for your situation
Production-Grade Generative AI Policy Design for Compliance Officers
Build audit-ready AI governance frameworks that scale with enterprise innovation
$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.
Compliance teams are expected to govern AI systems they didn’t build, using frameworks that weren’t designed for generative models.
The situation this course is for
Generative AI moves faster than legacy compliance infrastructure. Officers face pressure to deliver assurance without clear standards, consistent terminology, or implementation-grade tools. Generic guidelines don’t translate to audit-ready policies. The gap creates friction, delay, and misalignment between legal, risk, and engineering teams.
Who this is for
Compliance, risk, and governance professionals in technology-forward organizations who are expected to provide oversight on generative AI systems without inherited frameworks or clear implementation paths.
Who this is not for
This is not for developers building AI models, nor for executives seeking high-level AI strategy overviews. It’s not for practitioners outside compliance functions or those focused solely on traditional IT audit.
What you walk away with
- Design generative AI policies aligned with NIST, ISO, and emerging regulatory expectations
- Classify AI systems by risk tier and map controls accordingly
- Produce audit-ready documentation packages for internal and external reviewers
- Integrate policy design with model development lifecycles
- Lead cross-functional alignment between legal, risk, engineering, and product teams
The 12 modules (with all 144 chapters)
Module 1. Foundations of Generative AI for Compliance
Establish shared language and core technical concepts for governance.
12 chapters in this module
- Defining generative AI in enterprise context
- Distinguishing between models, applications, and interfaces
- Key differences from traditional ML systems
- Regulatory relevance of model provenance
- Understanding model inputs and training data origins
- Output characteristics and compliance implications
- Common deployment patterns in enterprise settings
- Vendor-hosted vs. in-house model use
- Lifecycle stages of generative AI systems
- Mapping model behavior to risk domains
- Compliance touchpoints across the stack
- Building cross-functional awareness
Module 2. Risk-Tiered Model Classification
Implement a consistent framework to categorize AI systems by impact.
12 chapters in this module
- Principles of risk-based classification
- Designing classification criteria
- High-risk model characteristics
- Medium and low-risk thresholds
- Incorporating legal jurisdictional factors
- Handling dual-use capabilities
- Dynamic reclassification triggers
- Documentation requirements by tier
- Model inventory design
- Ownership and stewardship assignment
- Change management integration
- Audit trail expectations
Module 3. Policy Architecture for Auditability
Structure policies to meet evidentiary standards in review cycles.
12 chapters in this module
- Designing for traceability
- Linking controls to documentation artifacts
- Standardizing policy language across teams
- Version control for policy assets
- Maintaining policy lineage
- Cross-referencing regulatory requirements
- Embedding policy into operational workflows
- Ensuring accessibility for auditors
- Role-based policy access design
- Change approval workflows
- Exception handling processes
- Policy sunsetting procedures
Module 4. Regulatory Horizon Scanning
Stay ahead of evolving requirements across jurisdictions.
12 chapters in this module
- Monitoring global AI regulatory developments
- Interpreting draft legislation for impact
- Tracking enforcement actions
- Benchmarking against voluntary frameworks
- Identifying jurisdictional overlaps
- Mapping proposed rules to existing systems
- Engaging with standards bodies
- Participating in public consultations
- Internal reporting on regulatory shifts
- Adjusting risk models based on policy trends
- Building regulatory scenario plans
- Communicating horizon risks to leadership
Module 5. Model Governance Frameworks
Establish oversight structures for model development and deployment.
12 chapters in this module
- Defining model owner responsibilities
- Setting up model review boards
- Approval workflows for model deployment
- Documentation standards for model cards
- Data lineage and provenance tracking
- Bias and fairness assessment protocols
- Performance monitoring expectations
- Versioning and rollback procedures
- Incident reporting frameworks
- Model decommissioning requirements
- Vendor model governance expectations
- Third-party audit integration
Module 6. Data Compliance in Generative Systems
Address privacy and data protection obligations specific to AI.
12 chapters in this module
- Training data provenance and rights
- Personal data in model outputs
- PII detection and redaction strategies
- Data minimization in prompts
- User consent in AI interactions
- Cross-border data transfer implications
- Right to explanation under AI use
- Data subject request handling
- Record of processing activities
- DPIA integration with AI deployment
- Vendor data handling assessments
- Audit preparedness for data teams
Module 7. Human-in-the-Loop Design
Ensure appropriate oversight in automated decision environments.
12 chapters in this module
- Defining meaningful human review
- Designing for human override
- Alerting mechanisms for AI outputs
- Role clarity in review processes
- Training for human reviewers
- Response time expectations
- Escalation pathways
- Documentation of human intervention
- Measuring review effectiveness
- Balancing automation and oversight
- Audit requirements for human review logs
- Scaling review processes with volume
Module 8. Explainability and Transparency Reporting
Meet stakeholder expectations for AI system clarity.
12 chapters in this module
- Defining explainability by use case
- Stakeholder communication strategies
- System transparency documentation
- Model behavior summaries
- Limitations and uncertainty reporting
- User-facing disclosure requirements
- Internal transparency standards
- Third-party explainability assessments
- Benchmarking against industry norms
- Dynamic updates to transparency reports
- Handling proprietary model constraints
- Audit trails for reporting accuracy
Module 9. Incident Response and Model Monitoring
Design proactive systems for detecting and managing AI issues.
12 chapters in this module
- Defining AI incidents and near misses
- Establishing detection mechanisms
- Thresholds for escalation
- Incident classification schema
- Response team roles and responsibilities
- Communication protocols
- Remediation workflows
- Model rollback procedures
- Post-incident review processes
- Reporting to regulators and stakeholders
- Maintaining incident logs
- Continuous monitoring integration
Module 10. Third-Party and Vendor AI Risk
Govern externally sourced models and AI services.
12 chapters in this module
- Vendor due diligence frameworks
- Assessing model transparency commitments
- Right-to-audit negotiation strategies
- Contractual risk allocation
- Service-level expectations for AI
- Monitoring vendor compliance
- Handling model updates and changes
- Incident response coordination
- Exit strategy planning
- Subcontractor oversight
- Geopolitical risk considerations
- Audit readiness for vendor relationships
Module 11. Cross-Functional Alignment Strategies
Lead collaboration between compliance, engineering, and product.
12 chapters in this module
- Building shared definitions across teams
- Establishing joint review processes
- Designing policy feedback loops
- Embedding compliance in development sprints
- Training engineering teams on policy goals
- Translating technical details for legal review
- Facilitating risk dialogues
- Conflict resolution frameworks
- Metrics for alignment success
- Leadership communication strategies
- Maintaining policy momentum
- Scaling governance across teams
Module 12. Scaling Governance Across the Organization
Expand policy design from pilot to enterprise-wide application.
12 chapters in this module
- Phased rollout planning
- Center of excellence design
- Policy automation tools
- Training and enablement programs
- Internal certification frameworks
- Metrics for governance maturity
- Executive reporting structures
- Budgeting for governance operations
- External benchmarking
- Continuous improvement cycles
- Knowledge sharing mechanisms
- Future-proofing governance design
How this maps to your situation
- When launching first generative AI pilot
- During regulatory scrutiny or audit preparation
- Scaling AI across business units
- Integrating third-party AI services
Before vs. after
Before
Overwhelmed by fragmented guidelines and reactive requests, struggling to apply static policies to dynamic systems.
After
Leading with structured, scalable frameworks that align engineering velocity with compliance assurance.
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 4 hours per module, designed for self-paced learning with implementation milestones.
If nothing changes
Without implementation-grade policy design, teams default to ad hoc reviews that slow innovation, increase audit findings, and create misalignment across risk, legal, and engineering functions.
How this compares to the alternatives
Unlike generic AI ethics guides or high-level strategy decks, this course delivers implementation-grade policy frameworks with templates and decision logic used by leading enterprises. It bridges the gap between principle and practice.
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, and governance professionals responsible for overseeing generative AI systems in technology-driven organizations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a certificate is issued upon finishing all modules and assessments.
$199 one-time. Approximately 4 hours per module, designed for self-paced learning with implementation milestones..
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