Skip to main content
Image coming soon

Cross-Functional Generative AI Policy Design for Audit Teams

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Cross-Functional Generative AI Policy Design for Audit Teams

Build implementation-grade AI governance frameworks that align audit, legal, and technical teams

$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.
AI governance initiatives stall without clear coordination between audit, technical, and compliance teams

The situation this course is for

Audit teams are being asked to assess AI systems they didn’t design, using standards that don’t yet exist. Meanwhile, engineering moves fast, legal requires precision, and leadership demands clarity. Without a shared framework, policy becomes fragmented, reactive, and hard to enforce.

Who this is for

Business and technology professionals in compliance, risk, governance, or audit functions who are leading or contributing to generative AI policy development across teams

Who this is not for

This course is not for individual contributors looking for high-level AI awareness or technical model training. It’s for those responsible for designing and operationalizing cross-functional policy.

What you walk away with

  • Design AI policy frameworks that audit teams can enforce and technical teams can implement
  • Map regulatory expectations to technical controls and operational workflows
  • Facilitate alignment sessions between legal, data, engineering, and audit stakeholders
  • Create reusable templates for AI risk assessments, policy exceptions, and control validation
  • Lead the development of an organization-wide generative AI governance playbook

The 12 modules (with all 144 chapters)

Module 1. Foundations of Generative AI Governance
Establish core principles, scope, and governance models for generative AI systems
12 chapters in this module
  1. Defining generative AI in the audit context
  2. Key differences from traditional AI and automation
  3. Governance vs. compliance: clarifying roles
  4. Regulatory landscape overview
  5. Emerging standards and frameworks
  6. Risk categories unique to generative AI
  7. The audit function’s evolving mandate
  8. Stakeholder mapping across functions
  9. Building the business case for governance
  10. Creating a cross-functional charter
  11. Establishing governance tiers
  12. Measuring policy maturity
Module 2. Cross-Functional Stakeholder Alignment
Align audit, legal, data, and engineering teams around shared AI policy goals
12 chapters in this module
  1. Identifying decision rights across functions
  2. Facilitating joint risk workshops
  3. Translating technical constraints into policy language
  4. Addressing legal and compliance thresholds
  5. Managing conflicting priorities
  6. Creating shared definitions and glossaries
  7. Building cross-functional communication protocols
  8. Running policy design sprints
  9. Documenting agreements and exceptions
  10. Establishing feedback loops
  11. Conflict resolution in policy development
  12. Sustaining alignment over time
Module 3. AI Risk Assessment for Audit Teams
Conduct structured risk assessments tailored to generative AI systems
12 chapters in this module
  1. Scoping AI systems under review
  2. Identifying high-risk use cases
  3. Data provenance and training set evaluation
  4. Output reliability and hallucination risks
  5. Bias and fairness assessment methods
  6. Security and prompt injection vulnerabilities
  7. Third-party model risk considerations
  8. Versioning and change control risks
  9. Human-in-the-loop requirements
  10. Scoring risk severity and likelihood
  11. Documenting risk assessments for audit
  12. Integrating findings into control design
Module 4. Policy Design and Documentation
Create clear, enforceable AI policies that technical teams can implement
12 chapters in this module
  1. Structuring policy hierarchies
  2. Writing actionable policy statements
  3. Defining roles and responsibilities
  4. Specifying technical requirements
  5. Incorporating auditability criteria
  6. Designing policy exception processes
  7. Version control and change management
  8. Policy communication strategies
  9. Creating policy implementation checklists
  10. Linking policy to control frameworks
  11. Using templates for consistency
  12. Maintaining policy repositories
Module 5. Control Design for Generative AI Systems
Develop audit-ready controls for generative AI deployment and monitoring
12 chapters in this module
  1. Mapping controls to AI risk categories
  2. Pre-deployment validation controls
  3. Input validation and filtering
  4. Output monitoring and logging
  5. Access control and authentication
  6. Model version tracking
  7. Human review and escalation paths
  8. Incident response for AI failures
  9. Third-party vendor controls
  10. Continuous monitoring strategies
  11. Control testing methodologies
  12. Documenting control effectiveness
Module 6. Audit Integration and Assurance
Integrate generative AI into existing audit programs and assurance cycles
12 chapters in this module
  1. Adapting audit plans for AI systems
  2. Sampling strategies for AI outputs
  3. Testing control design and operating effectiveness
  4. Evaluating model documentation
  5. Assessing training data quality
  6. Reviewing prompt engineering practices
  7. Auditing third-party AI services
  8. Reporting AI findings to leadership
  9. Coordinating with external auditors
  10. Maintaining audit trails
  11. Using automation in AI audits
  12. Building AI audit capability within teams
Module 7. Legal and Compliance Integration
Align AI policies with data privacy, intellectual property, and regulatory requirements
12 chapters in this module
  1. GDPR and data subject rights implications
  2. CCPA and state privacy law considerations
  3. Intellectual property ownership of AI outputs
  4. Copyright risks in training data
  5. Regulatory reporting obligations
  6. Sector-specific rules (e.g., financial services, healthcare)
  7. Export controls and cross-border data flows
  8. Contractual obligations with vendors
  9. Liability for AI-generated content
  10. Recordkeeping and retention policies
  11. Handling regulatory inquiries
  12. Staying current with evolving guidance
Module 8. Technical Implementation Readiness
Ensure policies are technically feasible and operationally sustainable
12 chapters in this module
  1. Engaging engineering teams early
  2. Evaluating model observability tools
  3. Logging and monitoring infrastructure
  4. Prompt chaining and workflow controls
  5. API security and rate limiting
  6. Model drift detection
  7. Fallback mechanisms and overrides
  8. Data retention and deletion
  9. Model retraining protocols
  10. Version rollback procedures
  11. Testing in staging environments
  12. Scaling controls across use cases
Module 9. Change Management and Adoption
Drive organization-wide adoption of AI policies and practices
12 chapters in this module
  1. Assessing organizational readiness
  2. Identifying champions and influencers
  3. Communicating policy changes effectively
  4. Training programs for different roles
  5. Addressing resistance and skepticism
  6. Measuring policy adoption rates
  7. Incentivizing compliance
  8. Handling policy violations
  9. Updating onboarding materials
  10. Creating feedback channels
  11. Iterating based on user input
  12. Celebrating early wins
Module 10. Incident Response and Escalation
Prepare for and respond to generative AI incidents effectively
12 chapters in this module
  1. Defining AI incident types
  2. Establishing detection mechanisms
  3. Creating incident response playbooks
  4. Escalation paths and decision authorities
  5. Containment and mitigation strategies
  6. Communication protocols during incidents
  7. Regulatory reporting timelines
  8. Post-incident review processes
  9. Updating policies based on incidents
  10. Simulating AI failure scenarios
  11. Documenting lessons learned
  12. Maintaining incident archives
Module 11. Scaling AI Governance Across the Organization
Expand policy frameworks from pilot projects to enterprise-wide programs
12 chapters in this module
  1. Creating a center of excellence
  2. Standardizing tools and platforms
  3. Building reusable policy components
  4. Onboarding new teams and use cases
  5. Integrating with enterprise risk management
  6. Aligning with digital transformation goals
  7. Budgeting for ongoing governance
  8. Hiring and upskilling talent
  9. Measuring program ROI
  10. Reporting to executive leadership
  11. Benchmarking against peers
  12. Iterating the governance model
Module 12. Sustaining and Evolving the Program
Maintain relevance and effectiveness as AI technology and regulations evolve
12 chapters in this module
  1. Monitoring regulatory developments
  2. Tracking technology trends
  3. Updating policies proactively
  4. Reassessing risk profiles
  5. Refreshing training content
  6. Conducting periodic audits
  7. Soliciting stakeholder feedback
  8. Benchmarking performance metrics
  9. Adapting to new use cases
  10. Retiring outdated policies
  11. Documenting evolution over time
  12. Planning for long-term sustainability

How this maps to your situation

  • When audit teams are asked to assess AI systems without clear standards
  • When legal and engineering teams disagree on policy feasibility
  • When AI initiatives outpace governance frameworks
  • When regulators begin inquiring about AI risk management

Before vs. after

Before
AI governance is reactive, fragmented, and siloed, audit teams lack clear policy, engineering resists constraints, and legal remains uninvolved.
After
You lead a unified, proactive AI governance program with clear policy, cross-functional alignment, and audit-ready controls across the organization.

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 4-6 hours per module, designed for steady implementation alongside full-time work.

If nothing changes
Without structured policy design, organizations face inconsistent enforcement, regulatory exposure, and erosion of trust in AI systems, especially as audit scrutiny increases.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementation-grade policy design tools specifically for audit and cross-functional teams, actionable from day one.

Frequently asked

Who is this course designed for?
It's for business and technology professionals in audit, compliance, risk, or governance roles who are leading or contributing to generative AI policy development across teams.
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 assessments.
$199 one-time. Approximately 4-6 hours per module, designed for steady implementation alongside full-time work..

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
!