A tailored course, built for your situation
Pragmatic Generative AI Policy Design for Compliance Officers
A 12-module implementation-grade course for professionals shaping AI governance in dynamic regulatory environments.
$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 being asked to govern fast-moving AI systems without clear frameworks or practical tools.
The situation this course is for
Generative AI is being deployed across functions faster than policy can keep up. Compliance officers face pressure to respond with robust governance, but most lack structured methods to assess risk, define controls, or coordinate across technical and business units. The result is reactive, fragmented policy that struggles to scale.
Who this is for
Mid-to-senior level compliance, risk, or governance professionals in technology-driven enterprises who are tasked with overseeing AI deployments and ensuring regulatory alignment.
Who this is not for
This course is not for individuals seeking high-level AI ethics discussions or technical model auditing. It is also not for those not involved in policy creation, implementation, or oversight.
What you walk away with
- Design enforceable generative AI policies tailored to organizational risk appetite
- Classify AI use cases by compliance impact and regulatory exposure
- Map controls across the model lifecycle from development to decommissioning
- Align internal policy with evolving global standards and sector-specific requirements
- Lead cross-functional alignment between legal, security, data, and product teams
The 12 modules (with all 144 chapters)
Module 1. Foundations of Generative AI in Regulated Environments
Establish core definitions, use case categories, and regulatory touchpoints for generative AI.
12 chapters in this module
- Defining generative AI in compliance terms
- Common deployment patterns in financial services
- Regulatory signals shaping AI governance
- Distinguishing AI policy from data and security policy
- The compliance officer’s role in AI governance
- Mapping stakeholder expectations
- Key differences from traditional automation
- Emerging standards and frameworks
- Risk taxonomy for generative models
- Use case segmentation by impact level
- Policy lifecycle stages
- Setting scope and boundaries
Module 2. Risk Assessment Frameworks for AI Systems
Apply structured methods to evaluate AI risk across confidentiality, integrity, and availability dimensions.
12 chapters in this module
- Principles of AI-specific risk assessment
- Designing risk scoring models
- Identifying high-impact failure modes
- Third-party model risk considerations
- Bias and fairness in generative outputs
- Hallucination and accuracy risk
- Supply chain transparency requirements
- Customer harm potential assessment
- Reputational exposure modeling
- Legal and regulatory violation likelihood
- Risk aggregation across portfolios
- Documentation standards for audit
Module 3. Policy Architecture and Governance Models
Build scalable governance structures that align with organizational maturity and risk profile.
12 chapters in this module
- Centralized vs. federated governance models
- Establishing AI review boards
- Policy ownership and accountability
- Integrating AI governance into existing frameworks
- Defining escalation pathways
- Version control and change management
- Policy exception handling
- Stakeholder communication protocols
- Metrics for governance effectiveness
- Board-level reporting frameworks
- Internal audit integration
- Continuous improvement loops
Module 4. Use Case Intake and Approval Workflows
Design standardized processes for evaluating and approving AI deployments.
12 chapters in this module
- Receiving and triaging AI project requests
- Pre-assessment screening criteria
- Required documentation from project teams
- Risk-based tiering of use cases
- Interim controls for pilot phases
- Cross-functional review checklists
- Third-party vendor evaluation
- Data provenance and licensing checks
- Human-in-the-loop requirements
- Fallback mechanism validation
- Approval lifecycle tracking
- Post-approval monitoring triggers
Module 5. Model Lifecycle Oversight
Implement controls across development, deployment, monitoring, and retirement phases.
12 chapters in this module
- Pre-development policy checkpoints
- Training data compliance requirements
- Model validation expectations
- Deployment authorization process
- Monitoring for drift and degradation
- Incident response for AI failures
- User feedback integration
- Version update controls
- Decommissioning and data erasure
- Audit trail preservation
- Model lineage tracking
- Lifecycle documentation standards
Module 6. Cross-Jurisdictional Compliance Alignment
Navigate overlapping regulations and design policies that meet global requirements.
12 chapters in this module
- GDPR and AI processing implications
- U.S. sector-specific guidance overview
- EU AI Act compliance mapping
- Asia-Pacific regulatory developments
- Cross-border data flow considerations
- Local customization vs. global standards
- Regulatory sandbox participation
- Engaging with supervisory bodies
- Transparency and disclosure rules
- Consumer rights and AI interactions
- Recordkeeping for multi-jurisdiction audits
- Harmonizing enforcement expectations
Module 7. Third-Party and Vendor Risk Management
Extend policy controls to external AI providers and integrated services.
12 chapters in this module
- Vendor due diligence for AI capabilities
- Contractual obligations for model behavior
- Right-to-audit provisions
- Performance and output monitoring
- Subprocessor transparency
- Incident notification requirements
- Model update governance
- Exit strategy and data portability
- Service level agreement alignment
- Compliance validation for SaaS AI tools
- Open-source model risk assessment
- Vendor risk scoring and tiering
Module 8. Monitoring, Auditing, and Continuous Control
Design ongoing oversight mechanisms to ensure policy adherence.
12 chapters in this module
- Real-time monitoring for policy violations
- Automated control validation
- Sampling and testing methodologies
- Internal audit coordination
- Key risk indicators for AI systems
- Dashboards for compliance leadership
- Anomaly investigation workflows
- Remediation tracking
- Periodic policy effectiveness reviews
- User behavior analytics
- Logging and retention requirements
- Audit evidence packaging
Module 9. Incident Response and Escalation Protocols
Prepare for AI-related failures with structured response and reporting plans.
12 chapters in this module
- Defining AI incident categories
- Detection and triage procedures
- Immediate containment actions
- Stakeholder notification timelines
- Regulatory reporting triggers
- Customer communication templates
- Root cause analysis frameworks
- Corrective action planning
- Escalation to executive leadership
- Post-incident review process
- Lessons learned integration
- Public disclosure considerations
Module 10. Training and Change Management for Policy Adoption
Drive organization-wide understanding and adherence to AI policies.
12 chapters in this module
- Audience segmentation for training
- Role-specific policy guidance
- Onboarding for AI project teams
- Ongoing awareness campaigns
- Knowledge assessment methods
- Feedback loops for policy improvement
- Leadership engagement strategies
- Incentivizing compliance behavior
- Addressing resistance to controls
- Measuring policy adoption rates
- Support resources and help desks
- Training content lifecycle
Module 11. Policy Documentation and Audit Readiness
Create clear, defensible documentation that supports regulatory scrutiny.
12 chapters in this module
- Standard operating procedure templates
- Control mapping to regulatory requirements
- Evidence collection frameworks
- Internal review and sign-off processes
- Document versioning and access control
- Third-party audit preparation
- Regulatory examination response
- Gap assessment reporting
- Remediation plan documentation
- Compliance attestation workflows
- Document retention policies
- Secure storage and access protocols
Module 12. Future-Proofing and Adaptive Governance
Build systems that evolve with technology and regulation.
12 chapters in this module
- Horizon scanning for regulatory changes
- Technology trend monitoring
- Policy stress testing
- Scenario planning for emerging risks
- Agile policy update cycles
- Feedback from enforcement actions
- Benchmarking against peers
- Investing in compliance capability
- Scaling governance with AI adoption
- Succession planning for oversight roles
- Innovation enablement through clarity
- Long-term vision for AI governance
How this maps to your situation
- You're evaluating AI use cases but lack a consistent review framework
- You're responding to AI deployments reactively rather than proactively
- Your policy doesn't clearly align with global regulatory expectations
- You need to demonstrate governance effectiveness to auditors or leadership
Before vs. after
Before
Policies are reactive, inconsistent, and difficult to enforce across teams.
After
You lead with a structured, auditable, and scalable AI governance framework.
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 of focused learning, designed for completion over 8, 12 weeks with flexible pacing.
If nothing changes
Without a structured approach, compliance teams risk fragmented oversight, increased audit findings, and diminished influence in AI decision-making.
How this compares to the alternatives
Unlike generic AI ethics courses or technical model audit guides, this program delivers a compliance-specific, implementation-ready framework tailored to the operational realities of governance professionals in regulated industries.
Frequently asked
Who is this course designed for?
Compliance, risk, and governance professionals in technology, financial services, healthcare, and other regulated sectors who are responsible for overseeing AI systems and ensuring policy adherence.
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 available after finishing all modules and passing the final assessment.
$199 one-time. Approximately 45, 60 hours of focused learning, designed for completion over 8, 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