A tailored course, built for your situation
Audit-Tested Generative AI Policy Design for Audit Teams
Implementation-grade policy frameworks for AI governance that pass internal and external scrutiny
$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 rush to publish AI policies without aligning them to actual audit requirements. This creates rework, compliance gaps, and loss of stakeholder trust when controls cannot be demonstrated with evidence.
Who this is for
Business and technology professionals responsible for AI governance, risk, compliance, or internal audit functions
Who this is not for
Individuals seeking introductory AI awareness content or technical model development training
What you walk away with
- Design generative AI policies aligned with internal audit control frameworks
- Map policy requirements to evidence collection workflows
- Classify AI use cases by risk tier with audit-appropriate criteria
- Build version-controlled policy documentation that tracks with system changes
- Anticipate auditor questions and prepare responsive control narratives
The 12 modules (with all 144 chapters)
Module 1. Foundations of Audit-Tested AI Policy
Establish the core principles of policy design that withstand review
12 chapters in this module
- Defining audit-readiness in AI governance
- Key differences between AI policy and traditional IT policy
- The role of policy in risk tier classification
- Aligning with NIST AI RMF and ISO 42001
- Stakeholder mapping for policy adoption
- Policy lifecycle management basics
- Common failure points in AI policy audits
- Building policy with evidence trails in mind
- Version control and change logging
- Integrating legal and regulatory inputs
- Documenting assumptions and limitations
- Setting success metrics for policy effectiveness
Module 2. Risk Tiering for Generative AI Systems
Classify AI applications by impact and complexity
12 chapters in this module
- Principles of risk-based AI categorization
- High-impact vs. low-impact use case definitions
- Data sensitivity scoring for generative models
- Output criticality assessment framework
- Human-in-the-loop requirements by tier
- Third-party model risk considerations
- Supply chain transparency thresholds
- Bias and fairness evaluation triggers
- Incident escalation paths by tier
- Documentation depth per risk level
- Control density mapping
- Tier transition protocols
Module 3. Control Framework Integration
Align AI policies with existing governance structures
12 chapters in this module
- Mapping AI controls to COSO and COBIT
- Integrating with SOC 2 trust principles
- GDPR and privacy-by-design alignment
- Linking to enterprise risk management (ERM)
- Crosswalking with ISO 27001 controls
- Financial reporting impact assessment
- Operational resilience considerations
- Change management integration
- Vendor oversight linkages
- Third-party audit evidence requirements
- Control ownership assignment models
- Testing frequency guidelines
Module 4. Policy Evidence Workflows
Design documentation trails that satisfy auditors
12 chapters in this module
- Evidence types accepted in AI audits
- Logs, artifacts, and metadata requirements
- Model card and data card standards
- Versioned decision records (VDRs)
- Change approval workflows
- Stakeholder sign-off documentation
- Automated evidence collection options
- Storage and retention policies
- Access controls for audit evidence
- Sampling methods for review
- Gap analysis reporting templates
- Remediation tracking systems
Module 5. Audit Communication Protocols
Prepare for internal and external auditor interactions
12 chapters in this module
- Anticipating auditor questions by control area
- Response drafting best practices
- Escalation paths for unresolved findings
- Pre-audit readiness checklists
- Mock audit facilitation
- Evidence package assembly
- Timeline management during review cycles
- Cross-functional coordination strategies
- Reporting audit outcomes to leadership
- Follow-up action tracking
- Lessons learned integration
- Continuous improvement feedback loops
Module 6. Policy Versioning and Change Management
Maintain policy integrity through system evolution
12 chapters in this module
- Triggers for policy updates
- Change impact assessment framework
- Stakeholder consultation protocols
- Version control systems for policy docs
- Change logs and rationale documentation
- Approval workflows for revisions
- Communication plans for updates
- Training requirements for new versions
- Legacy system exception handling
- Backward compatibility rules
- Deprecation timelines
- Archiving old policy versions
Module 7. Third-Party and Vendor AI Oversight
Extend policy to external AI providers
12 chapters in this module
- Vendor risk assessment for AI tools
- Contractual obligations for transparency
- Right-to-audit clauses
- Subprocessor disclosure requirements
- Performance benchmarking standards
- Incident notification SLAs
- Model update communication protocols
- Data handling compliance verification
- Exit strategy and data portability
- Shared responsibility model mapping
- Vendor audit evidence collection
- Consolidated oversight reporting
Module 8. Incident Response and Escalation
Define clear paths for AI-related issues
12 chapters in this module
- Defining AI incidents vs. anomalies
- Triage protocols by risk tier
- Notification requirements for stakeholders
- Regulatory reporting thresholds
- Forensic data preservation
- Root cause analysis methods
- Remediation action tracking
- Public communication guidelines
- Lessons learned documentation
- Control enhancement follow-up
- Escalation to board-level reporting
- Post-incident audit preparation
Module 9. Training and Adoption Strategies
Ensure policy understanding across teams
12 chapters in this module
- Role-based training content design
- Onboarding integration for new hires
- Refresher training cycles
- Knowledge assessment methods
- Policy acknowledgment workflows
- Feedback mechanisms for improvement
- Leadership endorsement tactics
- Change champion networks
- Communication channel selection
- Adoption metric tracking
- Barriers to compliance identification
- Incentive structures for adherence
Module 10. Board and Executive Reporting
Translate technical policy into strategic insight
12 chapters in this module
- Board-level AI risk dashboard design
- Executive summary writing standards
- Risk appetite alignment
- Key metric selection for oversight
- Incident reporting thresholds
- Trend analysis presentation
- Strategic initiative linkage
- Resource request justification
- Regulatory horizon scanning
- Benchmarking against peers
- Success story curation
- Forward-looking risk statements
Module 11. Cross-Functional Policy Alignment
Synchronize AI governance across departments
12 chapters in this module
- Legal and compliance coordination
- IT and security integration
- Data governance team collaboration
- Product and engineering alignment
- HR policy consistency
- Marketing and communications guidelines
- Finance and procurement linkage
- Customer support protocols
- Sales enablement content
- Privacy office coordination
- External affairs messaging
- Unified policy repository management
Module 12. Continuous Improvement and Maturity
Evolve policy practices over time
12 chapters in this module
- AI governance maturity models
- Internal assessment frameworks
- Benchmarking against industry standards
- Feedback loop integration
- Technology watch processes
- Regulatory change monitoring
- Policy effectiveness audits
- Stakeholder satisfaction surveys
- Control optimization techniques
- Resource allocation planning
- Innovation adoption criteria
- Long-term roadmap development
How this maps to your situation
- Designing a new AI governance framework from scratch
- Updating existing AI policies to meet audit demands
- Responding to internal audit findings on AI controls
- Preparing for external certification or compliance review
Before vs. after
Before
Policy documents exist but lack alignment with audit requirements, leading to repeated findings and remediation efforts.
After
Audit-ready AI policies with integrated evidence workflows, clear control mappings, and stakeholder alignment that pass review with minimal exceptions.
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 36 hours of focused learning, designed to be completed at your pace over 6, 8 weeks.
If nothing changes
Without structured policy design, organizations face repeated audit findings, increased remediation costs, and erosion of trust in AI governance capabilities.
How this compares to the alternatives
Unlike generic AI ethics guides or high-level strategy talks, this course provides implementation-grade policy design tools calibrated to actual audit expectations and control frameworks.
Frequently asked
Who is this course designed for?
It's for business and technology professionals leading AI governance, risk, compliance, or audit functions who need to build or refine audit-ready policies.
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 passing the final assessment.
$199 one-time. Approximately 36 hours of focused learning, designed to be completed at your pace 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