AAIA SUCCESS BLUEPRINT

An end-to-end AI audit course designed for busy IT audit and assurance professionals aiming for ISACA’s AAIA certification.

This program simplifies AI auditing and offers a clear, guided path to success — no AI, ML, or coding background required.

ARE YOU READY TO START?

Join a growing community of forward-thinking auditors, gain the skills to audit AI systems confidently, and ensure compliance in an AI-driven world.

Delivered through flexible, self-paced modules and supported by expert coaches, this step-by-step blueprint replaces confusion with clarity and equips you to lead in the AI era.

AAIA is designed for IT & IS Audit professionals with a CISA, CIA (IIA) or CPA (AICPA) certification looking to gain recognition for their expertise, assuring AI-driven processes comply to the highest industry standards.

• IT & IS Auditors
• Cybersecurity Professionals
• Risk & Compliance Officers
• Consultants & Advisory Professionals
• Professionals Pursuing Leadership in AI Auditing, Governance and Security

MODULE 1. Fundamentals of Artificial Intelligence (AI)

Gain a solid foundation in AI by exploring what it is, how it works, and the different categories of AI. This module sets the stage for deeper understanding throughout the course.

MODULE 2. Machine Learning (ML) & Its Types

Understand the key types of machine learning - supervised, unsupervised, and reinforcement learning and how they apply to AI systems in real-world auditing scenarios.

MODULE 3. Neural Networks & ML Models

Explore the structure of neural networks and commonly used AI/ML models. Learn how they influence system behavior, explainability, and audit requirements.

MODULE 4. AI Life Cycle (OECD) & Key Considerations

Discover the OECD AI system lifecycle stages and how to evaluate business cases, risks, and governance factors at each phase.

MODULE 5. AI Strategy, Governance & Principles

Learn how organizations develop AI strategies aligned with governance best practices, key principles like transparency and fairness, and role-based responsibilities.

MODULE 6. AI Risk Management, NIST AI RMF & OECD Classification

Dive into AI-specific risk management frameworks, including NIST AI RMF and OECD classifications. Understand how to assess, monitor, and manage AI risks effectively.

MODULE 7. Foundation of Data Governance & Privacy

Master the core concepts of data governance and privacy regulations critical to auditing AI systems that rely on personal and sensitive data.

MODULE 8. Global AI Standards, Laws & Ethics

Explore global AI laws, standards, and ethical considerations. Understand how intellectual property, human rights, and accountability intersect with AI..

MODULE 9. AI Data Management & Security Controls

Examine how data is managed, secured, and governed in AI systems, with a focus on data controls, integrity, and compliance.

MODULE 10. AI Development Lifecycle

Understand each stage of the AI development lifecycle, from planning and design to deployment and monitoring and how auditors evaluate controls at every step.

MODULE 11. Privacy & Security By Design

Learn how to embed privacy and security into AI systems from the ground up, following principles of proactive design and preventive risk control..

MODULE 12. Change Management & Supervising AI Systems

Explore the importance of managing changes in AI systems and ensuring proper supervision, traceability, and configuration control.

MODULE 13. AI Testing, Security & Incident Response

Delve into audit-oriented testing techniques, common AI vulnerabilities, and the incident response process using ISO 27035 as a reference.

MODULE 14. Planning & Core Concepts of AI Audit

Learn how to plan an AI audit, understand types of controls, evaluate real-world use cases, and define the scope for auditing AI systems.

MODULE 15. Conducting Audit Testing & Sampling

Explore various testing techniques and sampling methods tailored to AI audits. Understand how to design effective, risk-based audit procedures.

MODULE 16. Understanding Evidence Collection & Walkthrough

Master the techniques for collecting audit evidence, conducting walkthroughs, and holding effective interviews to validate AI controls and risks.

MODULE 17. Data Quality, Analytics & Reporting

Understand how to assess data quality in AI systems, apply analytics for audit insights, and prepare compelling reports for stakeholders.