AAIA Advanced in AI Audit Training Course

Artificial intelligence is transforming how organisations operate, but it also introduces new governance, risk, compliance and operational challenges. Audit and assurance professionals increasingly need the specialist knowledge to assess whether AI systems are appropriately governed, controlled and monitored.

Who this course is for

This AAIA – Advanced in AI Audit Training Course is designed to provide participants with the practical knowledge and advanced auditing techniques needed to address AI-specific risks, ensure compliance with regulatory and ethical standards and strengthen organisational resilience. By the end of the programme, participants will be equipped to plan, execute, and report on AI audits that drive responsible, secure, and transparent AI adoption.

This course is particularly suitable for:

• IT auditors;
• Internal and external audit professionals;
• Assurance professionals;
• Risk managers;
• GRC professionals;
• Compliance officers;
• Cyber security professionals involved in assurance;
• Audit and assurance leads; and
• Professionals responsible for evaluating AI governance, risk or controls.

What you’ll learn

The course begins by establishing a strong understanding of AI fundamentals, including machine learning, deep learning, Generative AI, AI lifecycles and the components that underpin modern AI systems. Delegates then explore how these technologies affect organisational risk and the audit approach.

Building on these foundations, the course examines AI governance frameworks, risk management, privacy, data governance, ethics and regulatory requirements. Delegates learn how to evaluate governance arrangements, accountability and organisational policies, and identify opportunities to strengthen AI oversight and control.

Course outline

• Domain 0: Introduction to AI and Its Building Blocks – Core concepts, AI types, lifecycle, Generative AI, and practical applications.
• Domain 1: AI Governance and Risk – Governance frameworks, risk management, privacy, ethics.
• Domain 2: AI Operations – Data management, lifecycle, change control, testing, incident response.
• Domain 3: AI Auditing Tools and Techniques – Audit planning, sampling, evidence collection, reporting.

Throughout the course, practical exercises and real-world scenarios help delegates translate theory into actionable audit and assurance activities.

By the end of the course, delegates will be able to:

• Understand AI fundamentals and their implications for audit and risk management;
• Evaluate AI governance frameworks, policies and accountability arrangements;
• Assess AI risk management, privacy, data governance and ethical controls;
• Audit AI data management, system lifecycles and change-management processes;
• Assess AI outputs for transparency, fairness and reliability;
• Identify vulnerabilities and evaluate AI incident-response readiness;
• Develop and execute risk-based AI audit plans;
• Apply sampling, testing, evidence-gathering and data-analysis techniques;
• Produce clear, actionable AI audit findings and recommendations; and
• Communicate effectively with technical teams, management and executive stakeholders.

Electronic courseware fully aligned with ISACA AAIA objectives and global standards is provided, including delegate notes, hands-on exercises, live case studies and post-course support.