A tailored course, built for your situation
Modern AI Audit Readiness for Audit Teams
A structured, implementation-grade path to auditing AI systems with confidence
$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 are being asked to assess AI systems without clear frameworks, consistent methods, or ready-to-use tools.
The situation this course is for
As AI adoption accelerates, audit functions face increasing pressure to provide assurance on complex, opaque systems. Traditional audit approaches don’t map cleanly to machine learning models, data pipelines, or dynamic decision engines. Without a structured, up-to-date methodology, teams risk inconsistent evaluations, overlooked risks, or delayed sign-offs.
Who this is for
Business and technology audit professionals in mid-to-senior roles, working in regulated or innovation-driven environments, who need to assess AI systems with rigor and repeatability.
Who this is not for
This course is not for software engineers building AI models, data scientists training algorithms, or executives seeking high-level AI strategy. It is specifically designed for auditors who need to evaluate, validate, and report on AI systems.
What you walk away with
- Apply a standardized AI risk classification framework across use cases
- Evaluate model documentation, data lineage, and validation practices
- Design audit procedures for bias, fairness, and performance drift
- Integrate AI audit checkpoints into existing compliance workflows
- Produce clear, evidence-backed audit findings for technical and non-technical stakeholders
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI in Business Contexts
Understand how AI is deployed across functions and the implications for audit scope and risk.
12 chapters in this module
- Defining AI, ML, and automation in practice
- Common AI use cases by industry
- Distinguishing between rule-based and adaptive systems
- Key components of an AI pipeline
- Roles and responsibilities in AI development
- Regulatory touchpoints for AI deployments
- Audit relevance of model types and architectures
- Lifecycle stages of AI systems
- Data dependency and quality implications
- Third-party AI and vendor risk
- Human-in-the-loop and oversight models
- Mapping AI to business objectives
Module 2. AI Risk Taxonomy and Classification
Develop a consistent method for categorizing AI risks by impact, sensitivity, and scale.
12 chapters in this module
- Principles of risk categorization
- High-impact vs. low-impact AI use cases
- Sensitivity levels for data and decisions
- Scoring model criticality
- Regulatory exposure by application type
- Reputation and operational risk factors
- Dynamic vs. static risk assessment
- Risk tiering for audit prioritization
- Cross-functional risk alignment
- Documenting risk classifications
- Updating classifications over time
- Aligning with enterprise risk frameworks
Module 3. Model Documentation and Provenance
Audit the completeness and reliability of model documentation and development history.
12 chapters in this module
- Required elements of model cards
- Tracking model versioning and updates
- Data sources and preprocessing logs
- Feature engineering transparency
- Validation and testing records
- Hyperparameter selection rationale
- Model performance benchmarks
- Third-party model disclosures
- Audit trails for retraining events
- Change control processes
- Model ownership and stewardship
- Verifying documentation authenticity
Module 4. Bias, Fairness, and Equity Evaluation
Assess AI systems for discriminatory outcomes using structured, repeatable methods.
12 chapters in this module
- Defining fairness in context
- Common bias types in training data
- Disparate impact analysis
- Protected attributes and proxies
- Statistical fairness metrics
- Segmented performance testing
- Bias mitigation techniques
- Human review protocols
- Stakeholder feedback loops
- Reporting bias findings
- Remediation tracking
- Fairness in multi-class models
Module 5. Performance Monitoring and Drift Detection
Evaluate how AI systems are monitored in production and respond to performance degradation.
12 chapters in this module
- Key performance indicators for AI models
- Monitoring data drift and concept drift
- Alerting thresholds and escalation paths
- Model decay over time
- Revalidation triggers
- Logging prediction outcomes
- Feedback mechanisms from end users
- A/B testing and shadow mode
- Model rollback procedures
- Incident response for AI failures
- Uptime and availability tracking
- Performance reporting cadence
Module 6. Explainability and Interpretability Standards
Assess whether AI decisions can be understood and justified by stakeholders.
12 chapters in this module
- Types of explainability methods
- Local vs. global interpretability
- SHAP, LIME, and surrogate models
- Model simplicity vs. accuracy trade-offs
- User-facing explanations
- Regulatory requirements for explainability
- Auditability of black-box models
- Documentation of interpretation results
- Stakeholder comprehension testing
- Explainability in high-stakes decisions
- Limitations and assumptions
- Third-party explainability tools
Module 7. Compliance Integration with Existing Frameworks
Map AI audit activities to established standards like SOC 2, ISO, GDPR, and HIPAA.
12 chapters in this module
- AI controls within SOC 2
- GDPR and automated decision-making
- HIPAA considerations for health AI
- Financial regulations and model risk
- ISO 38507 and AI governance
- Mapping AI risks to control domains
- Control design for AI-specific risks
- Evidence collection strategies
- Third-party audit alignment
- Reporting to compliance officers
- Audit trail retention policies
- Cross-border data and model deployment
Module 8. AI Audit Planning and Scoping
Design focused, risk-based audit plans for AI initiatives.
12 chapters in this module
- Identifying AI audit entry points
- Engaging with AI project teams
- Defining audit objectives and scope
- Resource and timeline planning
- Stakeholder alignment
- Risk-based sampling of models
- Document review protocols
- Interview guides for data scientists
- Technical testing approaches
- Coordination with IT audit
- Audit program development
- Managing scope creep
Module 9. Validation of Training and Testing Practices
Assess the rigor and integrity of model development and validation processes.
12 chapters in this module
- Train/validation/test split integrity
- Cross-validation methods
- Overfitting detection
- Data quality checks
- Labeling accuracy and consistency
- Synthetic data usage
- External validation datasets
- Challenge testing with edge cases
- Model benchmarking
- Reproducibility of results
- Validation team independence
- Documentation of test outcomes
Module 10. AI Incident Response and Remediation
Evaluate how organizations detect, respond to, and learn from AI-related incidents.
12 chapters in this module
- Defining AI incidents and near-misses
- Detection mechanisms
- Response team roles
- Root cause analysis methods
- Model rollback and containment
- Stakeholder communication
- Regulatory reporting obligations
- Remediation tracking
- Post-incident review processes
- Updating controls after incidents
- Learning from AI failures
- Insurance and liability considerations
Module 11. Third-Party and Vendor AI Audits
Assess AI systems developed or hosted by external providers.
12 chapters in this module
- Vendor risk assessment frameworks
- Contractual audit rights
- Third-party model documentation
- Subprocessor transparency
- Cloud infrastructure risks
- API security and monitoring
- Performance SLAs
- Data ownership and portability
- Right-to-audit clauses
- Onsite vs. remote audit options
- Validation of vendor claims
- Managing multi-vendor AI ecosystems
Module 12. Reporting and Stakeholder Communication
Produce clear, actionable audit findings for technical and non-technical audiences.
12 chapters in this module
- Structuring AI audit reports
- Executive summaries for leadership
- Technical appendices
- Visualizing model risks
- Recommendations with implementation paths
- Risk rating frameworks
- Follow-up and remediation tracking
- Presenting to audit committees
- Balancing transparency and confidentiality
- Handling disputed findings
- Version control for reports
- Archiving and retrieval
How this maps to your situation
- Auditing a live AI deployment in production
- Reviewing a new AI initiative before launch
- Assessing vendor-provided AI tools
- Integrating AI controls into annual audit planning
Before vs. after
Before
Uncertain how to approach AI systems with the same rigor as traditional audits, relying on ad-hoc methods and incomplete frameworks.
After
Equipped with a repeatable, standards-aligned methodology to audit AI systems confidently and produce clear, evidence-backed findings.
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, 50 hours of focused learning, designed for self-paced completion over 6, 8 weeks.
If nothing changes
Without a structured approach, audit teams risk inconsistent evaluations, overlooked model risks, or delayed assurance cycles, potentially undermining trust in AI governance.
How this compares to the alternatives
Unlike high-level overviews or academic treatments, this course provides implementation-grade detail tailored to audit practitioners, combining technical depth with practical templates and real-world validation methods.
Frequently asked
Who is this course designed for?
Audit professionals in business or technology roles who need to assess AI systems with rigor, consistency, and compliance alignment.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there video content?
No, the course is text-based with downloadable templates and examples to support hands-on application.
$199 one-time. Approximately 40, 50 hours of focused learning, designed for self-paced completion 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