A tailored course, built for your situation
Audit-Tested Generative AI Policy Design for Established Enterprises
Implementation-grade policy design for reliable, compliant, enterprise-scale AI deployment
$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 look good on paper but fail under audit scrutiny
The situation this course is for
Many organizations deploy generative AI with lightweight governance, only to face challenges when internal or external auditors request evidence of control, consistency, and compliance. Without structured policy design, teams face rework, delays, or rollback of AI initiatives during review cycles.
Who this is for
Business or technology professionals in regulated or complex organizations who are responsible for designing, reviewing, or approving generative AI policy frameworks
Who this is not for
Startups using AI in unregulated contexts, individual developers building personal tools, or teams focused only on model tuning without governance requirements
What you walk away with
- Design generative AI policies that survive real-world audit scrutiny
- Align controls with existing compliance frameworks (e.g., SOC 2, ISO, NIST, HIPAA)
- Document policy artifacts to meet evidentiary standards
- Coordinate cross-functionally between legal, security, engineering, and operations
- Simulate audit conditions to test policy resilience before deployment
The 12 modules (with all 144 chapters)
Module 1. Foundations of Audit-Tested AI Governance
Introduces core principles of policy design that withstand scrutiny, including accountability, traceability, and control verification.
12 chapters in this module
- Defining audit-readiness in AI policy
- Differences between policy and technical controls
- Regulatory expectations for AI transparency
- Mapping policy to enterprise risk appetite
- Key roles in policy development and review
- Lifecycle stages of AI governance
- Common audit findings in AI deployments
- Building policy with evidence in mind
- Stakeholder alignment fundamentals
- Documentation standards for compliance
- Version control and audit trails
- Integrating policy into governance frameworks
Module 2. Control Framework Alignment
Covers integration with established standards such as SOC 2, ISO 27001, NIST AI RMF, and HIPAA.
12 chapters in this module
- Overview of SOC 2 and AI relevance
- Mapping AI policy to Trust Service Criteria
- ISO 27001 controls applicable to AI
- NIST AI Risk Management Framework integration
- HIPAA considerations for generative AI
- GDPR and data subject rights
- COBIT the current cycle for AI governance
- Mapping controls across frameworks
- Control overlap and efficiency
- Gap analysis techniques
- Evidence collection strategies
- Control validation workflows
Module 3. Policy Design for High-Risk Use Cases
Focuses on applications in finance, healthcare, HR, and legal where errors have significant consequences.
12 chapters in this module
- Identifying high-risk AI applications
- Defining harm thresholds and mitigation
- Human-in-the-loop requirements
- Bias assessment integration
- Explainability as a control
- Redaction and data masking policies
- Audit logging for decision tracking
- Fallback procedures and overrides
- Incident response planning
- Third-party model risk
- Vendor oversight requirements
- End-user training obligations
Module 4. Documentation for Audit Evidence
Teaches how to structure policy artifacts so they serve as credible evidence during review.
12 chapters in this module
- Types of audit evidence in AI governance
- Document classification and retention
- Versioning and change logs
- Approval workflows and sign-offs
- Risk assessment documentation
- Control implementation records
- Testing and validation reports
- Incident logs and post-mortems
- Training completion tracking
- Policy exception management
- Automated evidence collection
- Preparing for auditor interviews
Module 5. Cross-Functional Coordination Models
Details how to align legal, security, engineering, compliance, and business units around policy execution.
12 chapters in this module
- Stakeholder mapping for AI policy
- Defining RACI matrices for governance
- Legal team engagement strategies
- Security team integration
- Engineering team handoffs
- Compliance team collaboration
- Executive reporting formats
- Board-level communication
- Conflict resolution frameworks
- Change management for policy updates
- Feedback loops across departments
- Escalation protocols
Module 6. Policy Testing and Simulation
Covers techniques to validate policy effectiveness before audit, including red teaming and tabletop exercises.
12 chapters in this module
- Designing audit simulations
- Red teaming policy gaps
- Tabletop exercise planning
- Scenario development for testing
- Evaluating policy under stress
- Identifying control weaknesses
- Remediation tracking
- Third-party audit prep services
- Internal audit coordination
- Mock interview preparation
- Corrective action planning
- Continuous improvement cycles
Module 7. Generative AI Risk Taxonomy
Builds a structured classification of risks specific to generative models and their deployment contexts.
12 chapters in this module
- Output hallucination risks
- Data leakage and memorization
- Prompt injection vulnerabilities
- Model drift and degradation
- Copyright and IP exposure
- Reputational harm scenarios
- Operational disruption risks
- Dependency on third-party APIs
- Supply chain integrity
- Model provenance tracking
- Fine-tuning data risks
- End-user misuse potential
Module 8. Model Lifecycle Governance
Covers policy requirements across development, deployment, monitoring, and retirement phases.
12 chapters in this module
- Pre-development risk assessment
- Model design review gates
- Development environment controls
- Code and configuration management
- Testing and validation standards
- Deployment approval workflows
- Monitoring and alerting policies
- Performance degradation thresholds
- Retraining and update protocols
- Model versioning standards
- Decommissioning procedures
- Archival and data retention
Module 9. Data Provenance and Lineage
Ensures traceability from training data through inference outputs for compliance and debugging.
12 chapters in this module
- Defining data provenance in AI
- Training data sourcing policies
- Data labeling integrity
- Third-party data vetting
- Inference input tracking
- Output watermarking techniques
- Chain-of-evidence frameworks
- Data lineage tooling
- Audit trail integration
- Chain-of-custody documentation
- Data retention and deletion
- Cross-border data flow rules
Module 10. Vendor and Third-Party Oversight
Covers policy design for organizations using external AI platforms or models.
12 chapters in this module
- Vendor risk assessment frameworks
- Contractual obligations for AI use
- SLA and performance monitoring
- Subprocessor transparency
- Model card review standards
- API security requirements
- Incident notification clauses
- Right-to-audit provisions
- Compliance certification tracking
- Exit strategy planning
- Multi-vendor coordination
- Third-party audit report review
Module 11. Incident Response and Remediation
Builds policy-aligned response plans for AI-related incidents including bias, errors, or misuse.
12 chapters in this module
- Defining AI incident types
- Detection and alerting policies
- Triage and escalation workflows
- Legal and regulatory reporting
- Public relations coordination
- Model rollback procedures
- Bias incident investigation
- User harm mitigation
- Root cause analysis methods
- Corrective action tracking
- Post-incident review formats
- Regulatory filing requirements
Module 12. Scaling Policy Across Enterprise Units
Teaches how to adapt and deploy consistent policy frameworks across divisions, geographies, or business lines.
12 chapters in this module
- Policy centralization vs. delegation
- Global vs. local compliance needs
- Industry-specific adaptations
- Business unit onboarding
- Training and certification programs
- Policy enforcement mechanisms
- Monitoring and audit sampling
- Performance dashboards
- Continuous improvement feedback
- Policy version management
- Change communication planning
- M&A integration considerations
How this maps to your situation
- An organization is preparing for its first internal AI audit
- A compliance team is updating governance frameworks to include generative AI
- A technology leader is scaling AI use across departments and needs consistent controls
- A risk officer is building an AI oversight function from the ground up
Before vs. after
Before
Uncertainty about how to structure AI policies that hold up under review, leading to reactive fixes and compliance delays
After
Confidence in designing and defending AI governance frameworks that meet auditor expectations and support scalable deployment
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 40 hours of structured learning, designed for professionals applying concepts in parallel with current responsibilities.
If nothing changes
Organizations that delay robust policy design risk audit findings, project rollbacks, or regulatory scrutiny when deploying generative AI at scale.
How this compares to the alternatives
Unlike general AI ethics courses or high-level strategy guides, this program delivers implementation-grade policy design with audit validation techniques used in regulated enterprise environments.
Frequently asked
Who is this course designed for?
Professionals in compliance, risk, governance, legal, security, or technology roles who are responsible for designing or reviewing generative AI policies in established organizations.
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 passing the final assessment.
$199 one-time. Approximately 40 hours of structured learning, designed for professionals applying concepts in parallel with current responsibilities..
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