A tailored course, built for your situation
Strategic AI Audit Readiness for Audit Teams
Master the implementation-grade framework for auditing AI systems with confidence and compliance
$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 face increasing pressure to assess AI systems without clear methodologies, consistent controls, or aligned frameworks.
The situation this course is for
AI adoption is accelerating, yet audit functions often lack structured approaches to evaluate model risk, data provenance, decision transparency, and compliance alignment. Without a tailored methodology, audit teams risk inconsistent assessments, delayed reporting, and diminished influence in AI governance discussions.
Who this is for
Business and technology professionals in audit, risk, compliance, or governance roles leading or contributing to AI assurance initiatives.
Who this is not for
This course is not for data scientists building models or executives seeking high-level AI strategy overviews.
What you walk away with
- Apply a standardized framework to assess AI systems across risk, ethics, compliance, and performance
- Design audit programs specific to machine learning pipelines and AI-driven decisioning
- Leverage control templates for model validation, data lineage, and bias testing
- Align AI audit activities with regulatory expectations and internal governance standards
- Lead cross-functional AI assurance efforts with confidence and clarity
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Audit
Establish core concepts, scope, and the evolving role of audit in AI governance.
12 chapters in this module
- Defining AI in the audit context
- Distinguishing AI from traditional software audits
- Key stakeholders in AI assurance
- Regulatory landscape overview
- Ethical considerations in AI auditing
- Risk categories unique to AI systems
- Audit lifecycle adaptation for AI
- Evidence standards for algorithmic decisions
- Terminology alignment across technical and audit teams
- Common misconceptions about AI audits
- Case study: Early AI audit challenges
- Building your AI audit mindset
Module 2. AI Governance and Oversight
Evaluate organizational structures and policies that support responsible AI.
12 chapters in this module
- AI governance frameworks
- Roles and responsibilities in AI oversight
- Board-level reporting for AI risk
- AI ethics committees and charters
- Policy development for AI use cases
- Third-party AI vendor governance
- Escalation paths for model failures
- Audit’s role in governance maturity
- Assessing AI inventory and cataloging
- Change management for AI systems
- Documentation standards for AI governance
- Audit checklist for governance readiness
Module 3. Model Risk Management Integration
Adapt model risk principles to modern AI and machine learning systems.
12 chapters in this module
- Extending MRMs to deep learning models
- Model development lifecycle review
- Pre-deployment validation requirements
- Ongoing monitoring and retesting
- Model performance thresholds
- Drift detection and response
- Model versioning and rollback plans
- Segregation of duties in AI development
- Independent validation expectations
- Documentation for model risk audits
- Stress testing AI under edge cases
- MRM audit program template
Module 4. Data Provenance and Quality Assurance
Audit the data foundations that shape AI behavior and outcomes.
12 chapters in this module
- Tracing data lineage in AI pipelines
- Assessing training data representativeness
- Bias detection in data sourcing
- Data preprocessing audit points
- Feature engineering transparency
- Data quality metrics for AI
- Third-party data vendor risks
- Synthetic data usage and validation
- Data retention and privacy alignment
- Audit trail requirements for data changes
- Sampling strategies for large datasets
- Data audit playbook
Module 5. Algorithmic Transparency and Explainability
Evaluate methods to make AI decisions interpretable and auditable.
12 chapters in this module
- Types of explainability: global vs local
- Model interpretability techniques
- SHAP, LIME, and other XAI tools
- Auditability of black-box models
- Documentation of model logic
- User-facing explanations
- Regulatory expectations for transparency
- Trade-offs between accuracy and explainability
- Testing explanation consistency
- Stakeholder communication of model behavior
- Explainability in high-risk domains
- Transparency audit framework
Module 6. Bias, Fairness, and Equity Assessment
Implement structured testing for discriminatory patterns in AI outputs.
12 chapters in this module
- Defining fairness in context
- Bias types: historical, representation, measurement
- Disparate impact analysis
- Fairness metrics and thresholds
- Testing across demographic groups
- Bias mitigation techniques
- Audit evidence for fairness claims
- Third-party fairness audits
- Handling edge cases in fairness testing
- Bias incident response planning
- Reporting bias findings to leadership
- Equity assessment template
Module 7. Operational Resilience and Monitoring
Ensure AI systems perform reliably and safely in production.
12 chapters in this module
- Production monitoring design
- Real-time performance dashboards
- Anomaly detection in AI outputs
- Incident response for AI failures
- Fallback mechanisms and human oversight
- System degradation indicators
- Logging and alerting standards
- Drift and concept shift detection
- Model retraining triggers
- Stress testing under load
- Resilience audit checklist
- Operational audit workflow
Module 8. Compliance and Regulatory Alignment
Map AI audits to current and emerging regulatory requirements.
12 chapters in this module
- Global AI regulatory trends
- EU AI Act implications for audit
- US sector-specific guidance
- Canadian and UK frameworks
- Privacy laws and AI interaction
- Industry-specific rules (finance, healthcare)
- Regulatory reporting for AI systems
- Audit evidence for compliance claims
- Gap analysis against regulatory standards
- Preparing for regulatory exams
- Compliance mapping tool
- Regulatory audit simulation
Module 9. Third-Party and Vendor AI Audits
Assess externally developed or hosted AI systems with confidence.
12 chapters in this module
- Vendor due diligence for AI
- Contractual audit rights
- Assessing vendor documentation
- Evaluating third-party model risk
- API security and monitoring
- Data sharing risks with vendors
- Subprocessor oversight
- Onsite vs remote vendor audits
- Standardized vendor assessment templates
- Managing black-box vendor models
- Vendor incident response coordination
- Third-party audit playbook
Module 10. Cross-Functional Collaboration
Lead effective coordination between audit, data science, and business teams.
12 chapters in this module
- Building trust with data science teams
- Translating technical details for auditors
- Facilitating joint risk assessments
- Establishing common terminology
- Scheduling audits within development cycles
- Feedback loops for model improvement
- Conflict resolution in AI audits
- Joint documentation standards
- Workshops for alignment
- Stakeholder communication plans
- Collaboration audit metrics
- Team alignment blueprint
Module 11. Audit Program Design and Execution
Develop and run AI-specific audit programs from planning to reporting.
12 chapters in this module
- Scoping AI audit engagements
- Risk-based prioritization of AI systems
- Planning evidence collection
- Sampling strategies for AI audits
- Interview guides for technical teams
- Document request templates
- Testing AI control effectiveness
- Observing model behavior in production
- Drafting findings with technical precision
- Reporting to technical and non-technical audiences
- Follow-up and remediation tracking
- Audit program builder tool
Module 12. Future-Proofing AI Audit Capability
Sustain and evolve your audit function’s AI readiness over time.
12 chapters in this module
- Building internal AI audit expertise
- Training paths for audit teams
- Knowledge management for AI audits
- Tooling and automation opportunities
- Benchmarking against peers
- Staying current with AI advancements
- Innovation in audit methodology
- Scaling AI audit capacity
- Measuring audit impact on AI governance
- Roadmap for capability growth
- Leadership communication strategy
- Capability maturity model
How this maps to your situation
- Auditing a live AI system with limited documentation
- Preparing for a regulatory review of AI use
- Assessing a third-party AI vendor for procurement
- Designing an internal AI audit function from scratch
Before vs. after
Before
Uncertainty in how to approach AI audits, reliance on ad hoc methods, and difficulty influencing AI governance discussions.
After
Confidence in executing structured, repeatable AI audits with clear documentation, stakeholder alignment, and regulatory defensibility.
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 flexible, self-paced learning.
If nothing changes
Continuing with ad hoc or generalized audit approaches may result in missed risks, inconsistent findings, and reduced credibility in AI governance conversations.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level overviews, this program delivers implementation-grade tools, audit-specific templates, and a structured methodology tailored to assurance professionals.
Frequently asked
Who is this course designed for?
Audit, risk, compliance, and governance professionals involved in assessing AI systems.
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 flexible, self-paced learning..
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