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Empowering Auditors to Navigate the Future of AI Governance and Compliance

As Artificial Intelligence continues to transform industries, organizations face new challenges in ensuring the ethical use, regulatory compliance, and risk management of AI systems. The Certified Internal AI Auditor (CIAIA) certification is designed to equip internal auditors, compliance professionals, and risk managers with the expertise to audit AI technologies effectively and responsibly.

This globally relevant certification provides a structured approach to understanding AI systems, evaluating AI risks, assessing governance frameworks, and conducting comprehensive AI audits across the AI lifecycle.

Module 1: Introduction to Artificial Intelligence and Auditing

• • Basics of Artificial Intelligence and Machine Learning
  • Types of AI (Narrow AI, General AI, Generative AI)
  • Key AI technologies (NLP, Computer Vision, Robotics, Expert Systems)
  • AI Lifecycle Overview
  • Importance and Scope of AI Auditing
  • Roles and Responsibilities of an AI Auditor

Module 2: AI Governance and Risk Management

• • AI Governance Frameworks (OECD, EU AI Act, ISO/IEC 42001)
  • Ethical Principles of AI (Fairness, Transparency, Accountability)
  • AI Risk Categories (Operational, Reputational, Legal, Technical)
  • AI Risk Assessment Techniques
  • AI Governance Policies and Procedures
  • Stakeholder Engagement and Escalation Mechanisms

Module 3: AI Regulatory and Compliance Landscape

• • Global AI Regulatory Overview (EU AI Act, US NIST AI RMF, ISO Standards)
  • Legal and Ethical Considerations (Bias, Discrimination, Consent)
  • Data Protection Laws (GDPR, HIPAA, CCPA) and AI
  • AI Audit Trail and Documentation Requirements
  • Emerging AI Laws and Cross-Border Issues

Module 4: Internal Audit Framework for AI Systems

• • Internal Audit Fundamentals and Methodologies
  • Risk-Based Audit Planning for AI
  • Control Objectives for AI Systems
  • Creating AI Audit Programs and Checklists
  • Auditing AI Governance, Model Management, and Data Quality
  • Sampling, Testing, and Interview Techniques for AI Systems

Module 5: Auditing the AI Lifecycle

• • Auditing AI Strategy and Business Alignment
  • Auditing Data Collection and Preprocessing
  • Auditing Model Development and Training
  • Auditing AI Model Deployment and Monitoring
  • Change Management and Continuous Learning in AI Systems
  • Auditing AI System Retirement and Decommissioning

Module 6: AI Model Explainability, Fairness, and Bias

• • Explainability Techniques (LIME, SHAP, Model Cards)
  • Bias Detection and Mitigation Strategies
  • Fairness Metrics and KPIs
  • Interpretable vs Black-box Models
  • Case Studies on Discrimination and Bias in AI Systems

Module 7: Cybersecurity and Data Integrity in AI Systems

• • AI-Specific Threats (Model Poisoning, Adversarial Attacks)
  • Data Integrity Controls
  • Secure AI System Design and Architecture
  • Logging, Monitoring, and Alerting for AI Systems
  • Incident Response Planning for AI Failures

Module 8: Reporting, Recommendations, and Continuous Improvement

• • AI Audit Report Preparation
  • Key Audit Findings and Risk Ratings
  • Communication with Management and the Board
  • Remediation and Follow-up Process
  • Continuous Improvement and Lessons Learned
  • Integration with Enterprise Risk Management (ERM)

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