The Certified AI Auditor Assessor (CAIAA) program equips professionals to independently evaluate AI systems, focusing on model performance, data integrity, and governance compliance. It provides tools and frameworks for auditing AI lifecycle stages, ensuring adherence to fairness, explainability, and accountability standards. Participants gain skills in threat modeling, conducting interviews with AI teams, and validating controls. This program is ideal for those seeking to deliver objective, third-party AI assessments across industries.
Audience:
- Independent auditors
- Compliance consultants
- Risk management professionals
- AI governance officers
- Data ethics specialists
- Technology assurance advisors
Learning Objectives:
- Understand AI lifecycle stages and control points
- Evaluate fairness, accountability, and explainability in AI
- Perform AI-specific threat modeling and risk analysis
- Collect and validate audit evidence from teams and systems
- Conduct effective interviews with ML engineering teams
Course Modules:
Module 1: AI Governance and Lifecycle Controls
- Introduction to AI lifecycle phases
- Mapping controls across model stages
- Aligning with ISO and NIST frameworks
- Identifying audit checkpoints
- Control maturity models
- Role of governance boards
Module 2: Auditing Fairness, Accountability, Explainability
- Defining fairness metrics
- Measuring explainability in models
- Auditing for bias mitigation practices
- Accountability frameworks
- Documentation requirements
- Case-based audit examples
Module 3: Risk and Threat Modeling for AI Systems
- Introduction to AI threat models
- Identifying attack surfaces in AI/ML
- Risk prioritization techniques
- Threat modeling templates
- Control verification techniques
- Vulnerability documentation
Module 4: Audit Evidence and Control Validation
- Types of AI audit evidence
- Data and model lineage tracking
- Logging and traceability controls
- Evidence sampling methods
- Checklists for validation
- Handling missing or weak evidence
Module 5: Interviewing AI and ML Development Teams
- Preparing audit interview questions
- Assessing ML pipelines via dialogue
- Identifying gaps through team input
- Interview documentation best practices
- Role-specific questioning (data scientists, engineers)
- Red flags in interviews
Module 6: Reporting and Remediation Planning
- Structuring audit reports
- Summarizing findings clearly
- Mapping findings to risks
- Suggesting corrective actions
- Communicating with stakeholders
- Follow-up audit planning
Exam Domains:
- AI Audit Planning and Scoping
- Risk Identification in AI Systems
- Evaluating Model Governance Controls
- AI Ethics, Accountability, and Compliance
- Evidence Collection and Verification
- Reporting, Documentation, and Remediation
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in the field of AI auditing. Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified AI Auditor Assessor (CAIAA).
Question Types:
- Multiple Choice Questions (MCQs)
- True/False Statements
- Scenario-based Questions
- Fill in the Blank Questions
- Matching Questions (Matching concepts or terms with definitions)
- Short Answer Questions
Passing Criteria:
To pass the Certified AI Auditor Assessor (CAIAA) Certification Training exam, candidates must achieve a score of 70% or higher.
Call to Action:
Advance your career in AI assurance. Enroll in the CAIAA Certification Program today and become a trusted AI auditor.