Advanced in AI Auditor (AAIA) Azerbaijan Eğitimi

This two-day, official ISACA Advanced in AI Audit (AAIA) instructor-led course provides Information Systems auditors with the essential knowledge to assess the design, governance, and operational risks associated with AI systems. The training helps auditors understand the impact of AI on enterprise environments, systems, and human decision-making. Participants will explore AI risk frameworks, auditing tools, and techniques through practical exercises, real-world case studies, and collaborative discussions. The course empowers professionals to navigate AI adoption confidently, ensuring assurance practices align with legal, ethical, and operational standards.

This course is designed for IT audit professionals who already hold CISA, CIA, or CPA certifications, although not mandatory. A working knowledge of audit methodology, risk management, and IT governance is expected. Familiarity with data handling and emerging technologies is recommended.

This course is intended for:

• Certified auditors such as CISA, CIA, or CPA professionals
• IT governance, compliance, privacy, and assurance teams
• Enterprise risk managers and internal auditors

Security professionals, and audit teams responsible for evaluating AI solutions and controls

By the end of this course, participants will be able to:

• Explain governance principles and evaluate AI-specific risks across the enterprise
• Assess the effectiveness of AI operations, lifecycle processes, and system monitoring
• Apply leading practices in data privacy, model testing, and ethical compliance
• Use AI-specific audit techniques for planning, execution, and reporting
• Collect, test, and analyse audit evidence using modern tools and AI-driven sampling
• Deliver audit outputs aligned with stakeholder needs and regulatory requirements

AI governance and risk

• Types of AI and machine learning models
• Overview of AI lifecycle from planning to decommissioning
• Business use cases, cost-benefit analysis, and deployment options
• Governance strategies, roles, and AI-specific policies
• AI training, awareness, and metrics
• AI risk landscape, identification, assessment, and monitoring
• Data governance for AI (classification, licensing, retention)
• Privacy and regulatory considerations (GDPR, consent, ownership)
• Ethical practices including bias, fairness, and transparency

AI operations

• AI-specific data management (collection, confidentiality, security)
• Managing AI lifecycle phases with development methodologies
• Privacy-by-design and security-by-design principles
• Change management considerations for AI environments
• Supervision strategies: human-in-the-loop, observability, hallucination
• Testing AI models: from conventional to AI-specific techniques
• Threats and vulnerabilities including model poisoning and prompt injection
• Incident response planning, assessment, containment, and review

AI auditing tools and techniques

• Planning and designing an AI audit
  • Identifying AI assets and controls
  • Common audit use cases and auditor training needs
• Audit testing and sampling
• AI testing methodologies and sampling strategies
• Expected outcomes and performance indicators
• Data handling, transformation, and extraction
• Walkthroughs, interviews, and AI-driven log analysis
• Ensuring clean, relevant data
• Applying sentiment analysis and advanced dashboards
• Advisory reporting, visuals, and automation
• Conducting audit follow-up and ensuring quality assurance
• Evidence collection techniques
• Data quality and analytics
• Reporting AI audit outcomes

Hands-on learning

Participants will:

• Analyse case studies simulating real-world AI audit challenges
• Perform structured walkthroughs and risk assessments
• Evaluate audit evidence using simulated data sets
• Apply concepts through guided scenarios on AI lifecycle, privacy, and governance
• Participate in instructor-led discussions to enhance cross-functional audit understanding