Experienced auditor or advisory who want to specialise in IT audit and governance.
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AAIA (ISACA Advanced in AI Audit™)

AAIA (ISACA Advanced in AI Audit™)

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This training prepares experienced audit and governance professionals to assess, audit, and provide assurance over artificial intelligence systems and AI-enabled operational environments.

The AAIA certification is designed specifically for professionals with IT audit or advisory experience who want to specialize in AI audit and governance.

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

[ Audit AI systems and AI-enabled operational environments. ]

[ Assess AI governance, risk, and compliance controls. ]

[ Evaluate AI operational resilience and ethical considerations. ]

[ Use AI-enhanced audit methodologies and tools. ]

Full programme information

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Domain 1 – AI Governance and Risk

AI Models, Considerations, and Requirements
  • Types of AI:
    • Generative AI
    • Predictive AI
    • Narrow AI
    • General AI
  • Machine learning and neural network fundamentals
  • AI algorithms and technical concepts
  • AI lifecycle management:
    • Planning and design
    • Data collection and processing
    • Model development and deployment
    • Monitoring and decommissioning
AI Governance and Program Management
  • AI strategy and governance alignment
  • AI-related roles and responsibilities
  • AI governance policies and procedures
  • Skills and competencies for AI oversight
  • Program metrics and governance indicators
AI Risk Management
  • AI threat landscape
  • AI-specific risks and vulnerabilities
  • Risk assessments and mitigation strategies
  • Risk appetite and prioritization
  • Continuous monitoring and performance metrics
Privacy, Data Governance, Ethics, and Regulation
  • Data governance and classification
  • Data retention and licensing
  • Privacy and consent management
  • Ethical AI considerations:
    • Bias and fairness
    • Transparency and explainability
    • Trust and safety
    • Human rights and IP considerations
  • AI regulations, standards, and frameworks

Domain 2 – AI Operations

AI Data Management and Solution Lifecycle
  • Data collection and quality management
  • Data confidentiality and security
  • AI solution development lifecycle
  • Privacy and security by design
  • Explainability and robustness
AI Change Management and Operational Supervision
  • AI-specific change management considerations
  • AI observability and monitoring
  • Human-in-the-loop (HITL) governance
  • AI hallucination management
  • AI operational readiness and sustainability
AI Testing, Threats, and Incident Management
  • Conventional and AI-specific testing methodologies
  • Bias and adversarial testing
  • AI-related threats:
    • Data poisoning
    • Prompt injection
    • Model theft and inversion
    • Vendor AI risks
  • AI incident response and recovery processes

Domain 3 – AI Auditing Tools and Techniques

AI Audit Planning and Design
  • AI asset and control identification
  • AI audit scoping and objectives
  • AI governance control reviews
  • AI use cases for audit enhancement
  • Generative AI in audit processes
AI Audit Testing and Sampling
  • AI audit methodologies
  • AI testing approaches
  • AI-assisted sampling techniques
  • Audit optimization and anomaly detection
  • AI audit outcome analysis
Audit Evidence Collection and Reporting
  • AI-enhanced data collection techniques
  • Structured and unstructured data analysis
  • AI-assisted interviews and walkthroughs
  • AI audit analytics and dashboards
  • Reporting, visualizations, and automated follow-up

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ADDED VALUE

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Audit AI systems and AI-enabled operational environments.
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Assess AI governance, risk, and compliance controls.
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Apply AI-specific audit methodologies and testing techniques.
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Use AI tools to improve audit efficiency and insight generation.
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Deliver assurance across the AI lifecycle.
Languages
English
Seats
16
Format
Hybrid
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