A tailored course, built for your situation
Mid-Market Generative AI Policy Design for Audit Teams
Implementation-grade policy design for audit professionals leading AI governance in mid-market organizations
$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.
Audit teams lack tailored frameworks to govern Generative AI use in mid-market environments with limited resources and high compliance expectations.
The situation this course is for
Mid-market audit teams are expected to deliver enterprise-grade AI governance but lack access to scalable, implementation-ready policy design tools. Existing guidance is either too theoretical or built for large enterprises with dedicated AI ethics boards and legal teams. This creates delays, inconsistent enforcement, and misalignment with operational risk standards.
Who this is for
Audit, risk, and compliance professionals in mid-market organizations designing or maintaining AI governance policies without dedicated AI ethics teams.
Who this is not for
Enterprise AI ethics leads with mature governance boards, developers building AI models, or individuals seeking certification in general data protection.
What you walk away with
- Design risk-based AI usage policies aligned with audit standards
- Map Generative AI controls to existing compliance frameworks (e.g., SOC 2, ISO 27001)
- Classify AI tools by organizational risk tier and audit priority
- Lead cross-functional policy rollouts with legal, IT, and operations
- Document and maintain AI policy inventories for audit readiness
The 12 modules (with all 144 chapters)
Module 1. Foundations of Generative AI in Mid-Market Audit Contexts
Establish core definitions, use case patterns, and governance challenges unique to mid-market audit environments.
12 chapters in this module
- Understanding Generative AI vs. traditional automation
- Audit implications of public vs. private AI models
- Common deployment patterns in mid-market firms
- Regulatory expectations for AI transparency
- Key differences: AI policy vs. data policy
- The role of audit in AI lifecycle oversight
- Defining 'responsible AI' for compliance teams
- Risk domains: hallucination, bias, leakage
- Stakeholder mapping for AI governance
- Internal control frameworks relevant to AI
- Benchmarking current audit team AI readiness
- Setting policy design success criteria
Module 2. AI Risk Tiering for Audit Prioritization
Classify AI applications by risk level to focus audit effort where it matters most.
12 chapters in this module
- Principles of risk-tiered AI classification
- High-risk indicators: customer impact, data sensitivity
- Medium-risk triggers: internal decision support tools
- Low-risk categories: content drafting, summarization
- Developing an AI risk scoring rubric
- Validating risk tiers with legal and compliance
- Dynamic reclassification protocols
- Audit trail requirements by tier
- Documentation standards for risk assessments
- Integrating risk tiers into policy language
- Training auditors on risk differentiation
- Maintaining tiering consistency across departments
Module 3. Policy Architecture for Generative AI Use
Build modular, enforceable AI policies tailored to audit oversight needs.
12 chapters in this module
- Core components of an AI usage policy
- Defining permitted vs. prohibited use cases
- User accountability and attribution standards
- Data handling rules for AI inputs and outputs
- Version control for policy documents
- Policy exception management
- Approval workflows for AI tool adoption
- Integrating AI policy with code of conduct
- Language for third-party AI vendor contracts
- Employee attestation mechanisms
- Policy dissemination strategies
- Auditability of policy compliance
Module 4. Control Mapping to Compliance Frameworks
Align AI policy controls with SOC 2, ISO 27001, and other audit-relevant standards.
12 chapters in this module
- SOC 2 Trust Services Criteria and AI
- Mapping AI controls to Security principle
- Availability considerations for AI systems
- Processing integrity in AI-generated outputs
- Confidentiality of prompts and responses
- Privacy framework alignment
- ISO 27001 Annex A controls for AI
- Access control requirements for AI tools
- Change management for AI model updates
- Incident response for AI-related breaches
- Logging and monitoring expectations
- Third-party risk management integration
Module 5. Audit-Specific AI Governance Controls
Design controls that support audit verification and evidence collection.
12 chapters in this module
- Defining auditable AI usage events
- Log retention requirements for AI activity
- User identification in AI interactions
- Prompt and response archiving standards
- Evidence collection for AI decision trails
- Sampling strategies for AI output review
- Automated control monitoring options
- Thresholds for manual audit intervention
- Documentation standards for AI audits
- Sampling frequency by risk tier
- Cross-functional validation protocols
- Reporting AI control effectiveness
Module 6. Generative AI Vendor Assessment for Auditors
Evaluate third-party AI tools through an audit lens.
12 chapters in this module
- Vendor due diligence checklist for AI
- Data ownership and licensing terms
- Model training data provenance
- Fine-tuning and customization risks
- API security and authentication
- Service-level agreements for AI uptime
- Right-to-audit clauses in contracts
- Subprocessor transparency
- AI model version disclosure
- Incident notification timelines
- Exit strategy and data portability
- Ongoing vendor monitoring plan
Module 7. Employee AI Use Policy Design
Create clear, enforceable guidelines for workforce AI interactions.
12 chapters in this module
- Defining personal vs. professional AI use
- Prohibited content generation categories
- Customer data handling rules
- Confidentiality in AI prompts
- Approval workflows for AI tool use
- Whitelisted vs. blacklisted AI tools
- AI use in HR and hiring contexts
- Marketing and public communication rules
- Social media AI content disclosure
- AI-assisted document drafting standards
- Monitoring employee AI activity
- Disciplinary actions for policy violations
Module 8. AI Policy Implementation Roadmap
Lead organization-wide rollout with audit oversight.
12 chapters in this module
- Stakeholder engagement planning
- Pilot program design for AI policy
- Change management for policy adoption
- Training content for different roles
- Communication timeline and channels
- Feedback collection mechanisms
- Policy versioning and update cycle
- Integration with onboarding programs
- Manager enablement for policy enforcement
- Metrics for policy adoption success
- Audit readiness checklist
- Post-implementation review process
Module 9. AI Incident Response for Audit Teams
Prepare for and respond to AI-related control failures.
12 chapters in this module
- Defining AI incidents and near misses
- Incident classification by impact level
- Reporting pathways for AI issues
- Initial response protocols
- Evidence preservation for AI events
- Root cause analysis for AI failures
- Notification requirements
- Regulatory reporting thresholds
- Corrective action tracking
- Lessons learned documentation
- Audit trail reconstruction
- Preventing recurrence
Module 10. Ongoing AI Policy Monitoring and Review
Maintain policy relevance as AI capabilities evolve.
12 chapters in this module
- Scheduled policy review cycles
- Triggers for unscheduled updates
- Monitoring AI regulatory developments
- Tracking new AI tool adoption
- User behavior trend analysis
- Control effectiveness assessment
- Benchmarking against peer policies
- Internal audit testing frequency
- External audit preparation
- Policy maturity model progression
- Stakeholder feedback integration
- Continuous improvement loop
Module 11. Cross-Functional AI Governance Collaboration
Align audit policy with legal, IT, and business units.
12 chapters in this module
- Defining roles: policy owner vs. enforcer
- Legal review integration points
- IT security collaboration
- HR policy alignment
- Finance and procurement coordination
- Marketing and communications oversight
- Product development input
- Facilitating governance committee meetings
- Conflict resolution framework
- Shared documentation repository
- Escalation pathways
- Joint training initiatives
Module 12. Scaling AI Policy Across Business Units
Adapt core policy for different departments while maintaining audit consistency.
12 chapters in this module
- Centralized vs. decentralized policy models
- Department-specific annexes
- Legal and compliance variations
- Regional adaptation considerations
- Language and translation needs
- Localized regulatory requirements
- Industry-specific risk factors
- Customization approval process
- Consistency auditing across units
- Reporting structure for global teams
- Technology enablement for policy tracking
- Lessons from multi-division rollout
How this maps to your situation
- Audit teams developing first AI policy
- Organizations updating existing policies for Generative AI
- Firms preparing for external AI compliance audits
- Companies scaling AI use across departments
Before vs. after
Before
Audit teams operate without standardized, implementation-ready frameworks to govern Generative AI use, leading to inconsistent enforcement and compliance uncertainty.
After
Audit professionals lead with confidence using a structured, risk-tiered policy design approach aligned with compliance standards and organizational scale.
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 total engagement, designed for incremental completion over six weeks with audit team workflows in mind.
If nothing changes
Without a tailored AI policy framework, audit teams risk inconsistent enforcement, regulatory scrutiny, and operational delays when addressing AI-related control gaps.
How this compares to the alternatives
Unlike generic AI ethics guidelines or enterprise-focused governance playbooks, this course provides audit-specific, implementation-grade policy design tools calibrated for mid-market resource constraints and compliance demands.
Frequently asked
Who is this course designed for?
Audit, risk, and compliance professionals in mid-market organizations who are responsible for designing or maintaining Generative AI governance policies.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course relevant for organizations outside the US?
Yes, the policy frameworks are designed to be adaptable to global compliance environments with local customization guidance included.
$199 one-time. Approximately 36 hours of total engagement, designed for incremental completion over six weeks with audit team workflows in mind..
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