ISACA Advanced in AI Audit (AAIA) Cert Prep

Course details

Prepare for the ISACA AAIA exam by gaining an in-depth understanding of AI governance, risk management, and the dinstinctions of each compared to traditional IT governance, to ensure AI processes align with industry standards. Explore global AI frameworks, including the OECD and ISO 42001, to understand international compliance requirements. Learn about methods to secure AI systems, address ethical considerations, and mitigate potential threats. Enhance your knowledge of auditing techniques, from evidence collection to interpreting findings and delivering effective audit reports. Discover emerging trends and integrate AI assurance into enterprise governance frameworks. Learn how to manage AI risks proactively, ensuring ethical and effective AI deployment. This course is ideal for IT audit professionals holding a CISA, CIA, or CPA certification and for those who are eager to validate their expertise and assure compliance in AI systems.

Concepts

Advanced AI Auditing Course Introduction

Welcome to AI auditing training course

Foundations of AI Governance and Risk Management AI Governance and Risk

Understanding the foundations of AI governance and risk
AI governance vs. traditional IT governance
AI risk landscape - Identification and mitigation strategies
Building trust, transparency, and accountability in AI systems
Understanding AI models - Foundations, capabilities, and use cases
Designing and operating AI governance programs in practice

Global AI Frameworks and Principles AI Governance and Risk

Organisation for Economic Co-operation and Development (OECD) AI Principles - A global framework for trustworthy AI
International Organization for Standards (ISO) AI standards - Guiding principles for international compliance
Introduction to the National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF)
NIST AI RMF - Core pillars, best practices, and strategic use
Implementing the NIST AI RMF - A step-by-step guide
Integrating risk management across the AI lifecycle
Global standards, ethical AI principles, and regulatory convergence
Validating trustworthiness testing techniques for risk, fairness, and robustness

International Organization for Standards (ISO) 42001 Deep Dive AI Governance and Risk

Deep dive into ISO 42001
Core components of an AI management system (AIMS)
Overview of the ten clauses in ISO 42001
ISO 42001 Annex A - AI-specific controls overview
ISO 42001 implementation journey
Mapping ISO 42001 to European Union (EU) AI Act, NIST AI RMF, OECD
Auditing ISO 42001 - Internal and external perspectives
Integrating an AI management system (AIMS) with ISO IEC 27001 and ISO 9001

AI Ethics, Bias, and Responsible Data Use AI Operations

AI threat modeling and adversarial risk assessment
Uncovering bias, overfitting, and reliability challenges in AI
Privacy and data governance in the context of AI
Understanding privacy risks and vulnerabilities in AI systems
Data-centric AI - Managing inputs for ethical and reliable outcomes

Securing AI Systems and Threat Mitigation AI Operations

Adversarial attacks on AI - Techniques, risks, and prevention
Safeguarding against data integrity attacks in AI pipelines
Malicious applications of AI - Threats to society and systems
Securing AI systems - Addressing threats, exploits, and systemic risks
AI incident response - Recovery strategies and failure management

Responsible AI Design and Lifecycle Oversight AI Operations

AI design and lifecycle management - From ideation to retirement
Engineering responsible AI - Methodologies and frameworks
Driving organizational change for effective AI integration
Post-deployment oversight - Monitoring and supervising AI outputs

Auditing AI Systems - Methods and Evidence AI Audit Tools and Techniques

Planning and designing audits for AI governance and risk
Audit testing and sampling techniques in AI environments
Collecting audit evidence in AI systems - Sources and techniques
Ensuring data quality and analytics integrity in AI audits
Interpreting AI audit findings and reporting results
Structuring and delivering effective AI audit reports
Translating AI audit insights into stakeholder-ready outcomes

Emerging Trends in AI Assurance and Compliance Emerging Topics

Auditing generative AI and large language models (LLMs)
Designing continuous audit frameworks for real-time AI oversight
Integrating AI audits into enterprise governance, risk, and compliance (GRC) platforms
Preparing for external inspections and regulatory audits of AI
Audit as a service (AaaS) for AI - Scalable embedded assurance models

AI in the Enterprise - Key Functional Integrations Emerging Topics

The role of AI in modern internal audit functions
AI and cybersecurity - Synergies, threats, and opportunities
AI in the supply chain - Risk, optimization, and transparency
Automation and workforce transformation - Addressing job displacement