A tailored course, built for your situation
Practical AI Risk Officer Capabilities for Audit Teams
Implementation-grade skills for audit professionals leading AI governance
$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 evaluate AI systems without clear frameworks, consistent terminology, or proven control patterns.
The situation this course is for
As AI adoption accelerates, audit functions face increasing pressure to provide assurance on models that operate outside traditional data governance boundaries. Without structured methodologies, assessments become inconsistent, time-intensive, and difficult to scale, leading to gaps in coverage and diminished stakeholder confidence.
Who this is for
Business and technology professionals in audit, compliance, risk, or governance roles who are stepping into AI oversight responsibilities and need actionable, implementation-ready frameworks.
Who this is not for
This course is not for data scientists building models or executives seeking high-level AI strategy overviews. It is designed for practitioners who execute audits and need concrete tools to assess AI risk systematically.
What you walk away with
- Apply a standardized AI risk taxonomy aligned with global frameworks
- Document model workflows and decision logic using audit-ready templates
- Design and test controls for data quality, bias detection, and model drift
- Coordinate cross-functionally with data science and engineering teams effectively
- Produce clear, defensible audit findings for technical and non-technical stakeholders
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Risk for Auditors
Introduce core concepts of AI risk, audit relevance, and governance frameworks.
12 chapters in this module
- Defining AI risk in audit contexts
- Key differences between traditional and AI-enabled systems
- Overview of global AI governance standards
- Regulatory trends shaping audit expectations
- The role of the auditor in AI oversight
- Risk domains: fairness, transparency, accountability
- Common failure modes in AI systems
- Terminology alignment across technical and audit teams
- Stakeholder mapping for AI audits
- Preparing for AI audit scoping
- Ethical considerations in automated decision-making
- Building your AI audit mindset
Module 2. AI Risk Taxonomy and Classification
Develop a consistent classification system for AI risks across audit engagements.
12 chapters in this module
- Creating a standardized risk taxonomy
- Mapping risks to business impact levels
- Categorizing model types by audit complexity
- High-risk vs. general-purpose AI systems
- Data dependency risk classification
- Model update and retraining risks
- Third-party model risk assessment
- Human-in-the-loop risk patterns
- Scoring risk severity and likelihood
- Linking taxonomy to control objectives
- Versioning and maintaining the taxonomy
- Using taxonomy in audit planning
Module 3. Model Documentation and Audit Trails
Establish requirements for model documentation that support auditability.
12 chapters in this module
- Minimum viable model documentation
- Model cards and their audit utility
- System design specifications for auditors
- Data lineage and provenance tracking
- Version control for models and datasets
- Change management logs for AI systems
- Validating completeness of documentation
- Gaps in vendor-provided documentation
- Creating audit-ready documentation packages
- Documenting assumptions and limitations
- Metadata standards for AI systems
- Automating documentation collection
Module 4. Data Quality and Integrity Controls
Audit data pipelines and quality assurance practices supporting AI systems.
12 chapters in this module
- Assessing data representativeness
- Identifying data leakage risks
- Validating feature engineering processes
- Testing for data drift and concept drift
- Reviewing data cleaning and transformation rules
- Auditing data access and privacy controls
- Sampling strategies for large datasets
- Verifying label quality in supervised models
- Evaluating synthetic data usage
- Documenting data quality thresholds
- Assessing data pipeline monitoring
- Reporting data quality findings
Module 5. Bias Detection and Fairness Testing
Implement structured methods to detect and evaluate algorithmic bias.
12 chapters in this module
- Defining fairness in organizational context
- Common bias types in training data
- Performance disparity analysis across groups
- Selecting appropriate fairness metrics
- Testing for disparate impact
- Pre-processing, in-processing, post-processing controls
- Bias mitigation technique validation
- Audit procedures for explainability tools
- Reviewing fairness testing documentation
- Handling edge cases in fairness assessment
- Reporting bias findings to stakeholders
- Integrating fairness into ongoing monitoring
Module 6. Model Performance and Reliability
Evaluate model accuracy, stability, and operational reliability.
12 chapters in this module
- Understanding model evaluation metrics
- Assessing performance decay over time
- Testing for overfitting and underfitting
- Validating cross-validation practices
- Reviewing model calibration and confidence scores
- Auditing A/B testing and experimentation
- Monitoring production model performance
- Assessing fallback and redundancy mechanisms
- Stress testing model behavior
- Evaluating model interpretability methods
- Reviewing model error analysis practices
- Documenting performance thresholds
Module 7. Explainability and Interpretability
Assess the adequacy of model explanations for audit and regulatory purposes.
12 chapters in this module
- Types of explainability methods
- Local vs. global interpretability
- SHAP, LIME, and other tool limitations
- Evaluating explanation fidelity
- User comprehension of model outputs
- Regulatory expectations for explanations
- Auditing black-box model disclosures
- Testing consistency of explanations
- Explainability in high-stakes decisions
- Documentation of explanation processes
- Third-party explainability tool validation
- Reporting on explainability gaps
Module 8. Control Design and Testing
Design and test controls specific to AI system risks.
12 chapters in this module
- Mapping risks to control objectives
- Preventive, detective, and corrective controls
- Automated vs. manual control points
- Testing control effectiveness in AI workflows
- Reviewing model approval and deployment gates
- Auditing model monitoring dashboards
- Validating alerting and escalation procedures
- Assessing human oversight mechanisms
- Control testing for real-time models
- Sampling techniques for AI control audits
- Documenting control deficiencies
- Reporting control improvements
Module 9. Third-Party and Vendor Risk
Assess risks associated with external AI models and platforms.
12 chapters in this module
- Classifying third-party AI solutions
- Reviewing vendor model documentation
- Assessing vendor change management
- Auditing API security and reliability
- Evaluating vendor monitoring practices
- Validating service level agreements
- Assessing subcontractor and supply chain risks
- Testing vendor incident response plans
- Reviewing audit rights and access
- Assessing data ownership and portability
- Managing multi-tenant environment risks
- Documenting vendor risk findings
Module 10. Incident Response and Model Remediation
Audit incident response plans specific to AI system failures.
12 chapters in this module
- Defining AI incident types
- Reviewing detection and classification procedures
- Assessing escalation pathways
- Auditing model rollback and retraining
- Testing communication protocols
- Reviewing root cause analysis practices
- Evaluating remediation timelines
- Assessing stakeholder notifications
- Documenting incident resolution
- Post-mortem review processes
- Improving response through simulation
- Reporting incident response effectiveness
Module 11. Cross-Functional Coordination
Facilitate effective collaboration between audit, data science, and engineering teams.
12 chapters in this module
- Building trust across technical teams
- Translating audit requirements into technical terms
- Aligning on risk tolerance levels
- Coordinating audit timelines with model cycles
- Facilitating model review meetings
- Documenting cross-functional feedback
- Resolving disputes over risk ratings
- Sharing audit findings constructively
- Creating feedback loops for improvement
- Engaging legal and compliance partners
- Managing executive reporting alignment
- Sustaining collaboration over time
Module 12. Audit Reporting and Continuous Improvement
Produce clear, actionable reports and evolve AI audit practices.
12 chapters in this module
- Structuring AI audit reports
- Tailoring communication to audience
- Presenting technical findings clearly
- Linking findings to business impact
- Recommending actionable remediation steps
- Prioritizing risk mitigation efforts
- Tracking issue resolution
- Benchmarking against industry standards
- Updating audit programs based on findings
- Incorporating lessons learned
- Scaling AI audit capabilities
- Leading continuous improvement
How this maps to your situation
- Auditing AI systems in regulated environments
- Assessing third-party AI vendor solutions
- Evaluating internal AI model development pipelines
- Reporting AI risks to executive and board stakeholders
Before vs. after
Before
Uncertainty in how to approach AI systems during audits, inconsistent methods, and difficulty communicating technical risks to stakeholders.
After
Confidence in applying structured, repeatable methods to audit AI systems, with clear documentation, control testing, and stakeholder reporting.
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 45, 60 hours of self-paced learning, designed to fit around professional responsibilities.
If nothing changes
Without structured AI audit capabilities, teams risk inconsistent assessments, missed vulnerabilities, and reduced credibility when providing assurance on high-impact systems.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level strategy guides, this program focuses on implementation-grade audit practices with templates, checklists, and real-world examples tailored to compliance and risk professionals.
Frequently asked
Who is this course designed for?
Audit, compliance, and risk professionals who need practical, actionable methods to assess AI systems as part of their assurance responsibilities.
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 awarded after finishing all modules and passing the final assessment.
$199 one-time. Approximately 45, 60 hours of self-paced learning, designed to fit around professional responsibilities..
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