{"product_id":"isaca-advanced-in-ai-audit-aaia-certification","title":"ISACA Advanced in AI Audit (AAIA) Certification","description":"\u003cdiv\u003e\u003cp\u003eThis two-day, instructor-led course provides IS auditors with the foundational knowledge and background of AI solutions to evaluate their proper governance, design, development, and security to apply their expertise in audit and assurance activities in the enterprise. The course is structured to align with the job practice and features a variety of knowledge check questions, case studies, activities, and discussions designed to apply the concepts to real-life business scenarios.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eISACA Advanced in AI Audit (AAIA) Certification Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course, you will:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplain the principles of AI Governance and Risk Management\u003c\/li\u003e\n\u003cli\u003eImplement effective AI Operations practices\u003c\/li\u003e\n\u003cli\u003eUtilize AI Auditing Tools and Techniques\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003ePrerequisites\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eIT Audit professionals with a CISA, CIA, or CPA certification looking to enhance their expertise in navigating AI-driven challenges while upholding the highest industry standards.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eISACA AI Audit Certification Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eDomain 1. AI Governance and Risk\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eLearning Objectives:\u003c\/p\u003e\n\u003cp\u003eWithin this domain, the AI auditor should be able to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluate impacts, opportunities, and risk when integrating AI solutions within the audit process.\u003c\/li\u003e\n\u003cli\u003eEvaluate AI solutions to advise on impact, opportunities, and risk to organization.\u003c\/li\u003e\n\u003cli\u003eEvaluate the impact of AI solutions on system interactions, environment, and humans.\u003c\/li\u003e\n\u003cli\u003eEvaluate the role and impact of AI decision-making systems on the organization and stakeholders.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s AI policies and procedures, including compliance with legal and regulatory requirements.\u003c\/li\u003e\n\u003cli\u003eEvaluate the monitoring and reporting of metrics (e.g., KPIs, KRIs) specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate whether the organization has defined ownership of AI-related risk, controls, procedures, decisions, and standards.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s data governance program specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s privacy program specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s problem and incident management programs specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s change management program specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s configuration management program specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s threat and vulnerability management programs specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s identity and access management program specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate vendors and supply chain management program specific to AI solutions.\u003c\/li\u003e\n\u003cli\u003eEvaluate the design and effectiveness of controls specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate data inputs requirements for AI models (e.g., data appropriateness, bias, and privacy).\u003c\/li\u003e\n\u003cli\u003eEvaluate system\/business requirements for AI solutions to ensure alignment with enterprise architecture.\u003c\/li\u003e\n\u003cli\u003eEvaluate AI solution life cycle (e.g., design, development, deployment, monitoring, and decommissioning) and inputs\/outputs for compliance and risk.\u003c\/li\u003e\n\u003cli\u003eEvaluate algorithms and models to ensure AI solutions are aligned to business objectives, policies, and procedures.\u003c\/li\u003e\n\u003cli\u003eAnalyze the impact of AI on the workforce to advise stakeholders on how to address AI-related workforce impacts, training, and education.\u003c\/li\u003e\n\u003cli\u003eEvaluate that awareness programs align to the organization’s AI-related policies and procedures.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection A. AI Models, Considerations, and Requirements\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Types of AI\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eGenerative\u003c\/li\u003e\n\u003cli\u003ePredictive\u003c\/li\u003e\n\u003cli\u003eNarrow\u003c\/li\u003e\n\u003cli\u003eGeneral\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Machine learning\/AI Models\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eBasic models\u003c\/li\u003e\n\u003cli\u003eNeural networks\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e3. Algorithms\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eClasses of Algorithms\u003c\/li\u003e\n\u003cli\u003eAdditional AI Considerations (technical terms and concepts relevant to the IS auditor)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e4. AI Lifecycle Overview\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlan and Design\u003c\/li\u003e\n\u003cli\u003eCollect and Process Data\u003c\/li\u003e\n\u003cli\u003eBuild and\/or Adapt Model(s)\u003c\/li\u003e\n\u003cli\u003eTest, Evaluate, Verify, and Validate\u003c\/li\u003e\n\u003cli\u003eMake Available for Use\/Deploy\u003c\/li\u003e\n\u003cli\u003eOperate and Monitor\u003c\/li\u003e\n\u003cli\u003eRetire\/Decommission\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e5. Business Considerations\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eBusiness Use Cases, Needs, Scope, and Objectives\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\n\u003cli\u003eCost-Benefit Analysis\u003c\/li\u003e\n\u003cli\u003eReturn on Investment\u003c\/li\u003e\n\u003cli\u003eInternal vs. Cloud Hosting\u003c\/li\u003e\n\u003cli\u003eVendors\u003c\/li\u003e\n\u003cli\u003eShared Responsibility\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection B. AI Governance and Program Management\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. AI Strategy\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eStrategies\u003c\/li\u003e\n\u003cli\u003eOpportunities\u003c\/li\u003e\n\u003cli\u003eVision and Mission\u003c\/li\u003e\n\u003cli\u003eValue Alignment\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. AI-related Roles and Responsibilities\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eCategories, Focuses, and Common Examples\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e3. AI-related Policies and Procedures\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUsage Policies\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e4. AI Training and Awareness\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eSkills, Knowledge, and Competencies\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e5. Program metrics\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExamples of Metrics with Objectives and Definitions\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection C. AI Risk Management\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. AI-related Risk Identification\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAI Threat Landscape\u003c\/li\u003e\n\u003cli\u003eAI Risks\u003c\/li\u003e\n\u003cli\u003eChallenges for AI Risk Management\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Risk Assessment\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eRisk Assessment\u003c\/li\u003e\n\u003cli\u003eRisk Appetite and Tolerance\u003c\/li\u003e\n\u003cli\u003eRisk Mitigation and Prioritization\u003c\/li\u003e\n\u003cli\u003eRemediation Plans\/Best Practices\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e3. Risk Monitoring\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eContinuous Improvement\u003c\/li\u003e\n\u003cli\u003eRisk and Performance Metrics\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection D. Privacy and Data Governance Programs\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Data Governance\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eData Classification\u003c\/li\u003e\n\u003cli\u003eData Clustering\u003c\/li\u003e\n\u003cli\u003eData Licensing\u003c\/li\u003e\n\u003cli\u003eData Cleansing and Retention\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Privacy Considerations\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eData Privacy\u003c\/li\u003e\n\u003cli\u003eData Ownership (Governance and Privacy)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e3. Privacy Regulatory Considerations\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eData Consent\u003c\/li\u003e\n\u003cli\u003eCollection, Use, and Disclosure\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection E. Leading Practices, Ethics, Regulations, and Standards for AI\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Standards, Frameworks, and Regulations Related to AI\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eBest Practices\u003c\/li\u003e\n\u003cli\u003eIndustry Standards and Frameworks\u003c\/li\u003e\n\u003cli\u003eLaws and Regulations\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Ethical Considerations\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eEthical Use\u003c\/li\u003e\n\u003cli\u003eBias and Fairness\u003c\/li\u003e\n\u003cli\u003eTransparency and Explainability\u003c\/li\u003e\n\u003cli\u003eTrust and Safety\u003c\/li\u003e\n\u003cli\u003eIP Considerations\u003c\/li\u003e\n\u003cli\u003eHuman Rights\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDomain 2. AI Operations\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eLearning Objectives:\u003c\/p\u003e\n\u003cp\u003eWithin this domain, the AI auditor should be able to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluate impacts, opportunities, and risk when integrating AI solutions within the audit process.\u003c\/li\u003e\n\u003cli\u003eEvaluate AI solutions to advise on impact, opportunities, and risk to organization.\u003c\/li\u003e\n\u003cli\u003eEvaluate the impact of AI solutions on system interactions, environment, and humans.\u003c\/li\u003e\n\u003cli\u003eEvaluate the role and impact of AI decision-making systems on the organization and stakeholders.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s AI policies and procedures, including compliance with legal and regulatory requirements.\u003c\/li\u003e\n\u003cli\u003eEvaluate the monitoring and reporting of metrics (e.g., KPIs, KRIs) specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate whether the organization has defined ownership of AI-related risk, controls, procedures, decisions, and standards.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s data governance program specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s privacy program specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s problem and incident management programs specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s change management program specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s configuration management program specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s threat and vulnerability management programs specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate the organization’s identity and access management program specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate vendors and supply chain management program specific to AI solutions.\u003c\/li\u003e\n\u003cli\u003eEvaluate the design and effectiveness of controls specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate data inputs requirements for AI models (e.g., data appropriateness, bias, and privacy).\u003c\/li\u003e\n\u003cli\u003eEvaluate system\/business requirements for AI solutions to ensure alignment with enterprise architecture.\u003c\/li\u003e\n\u003cli\u003eEvaluate AI solution life cycle (e.g., design, development, deployment, monitoring, and decommissioning) and inputs\/outputs for compliance and risk.\u003c\/li\u003e\n\u003cli\u003eEvaluate algorithms and models to ensure AI solutions are aligned to business objectives, policies, and procedures.\u003c\/li\u003e\n\u003cli\u003eAnalyze the impact of AI on workforce to advise stakeholders to address AI-related workforce impacts, training, and education.\u003c\/li\u003e\n\u003cli\u003eEvaluate that awareness programs align to the organization’s AI-related policies and procedures.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection A. Data Management Specific to AI\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Data Collection\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eConsent\u003c\/li\u003e\n\u003cli\u003eFit for Purpose\u003c\/li\u003e\n\u003cli\u003eData Lag\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Data Classification\u003c\/p\u003e\n\u003cp\u003e3. Data Confidentiality\u003c\/p\u003e\n\u003cp\u003e4. Data Quality\u003c\/p\u003e\n\u003cp\u003e5. Data Balancing\u003c\/p\u003e\n\u003cp\u003e6. Data Scarcity\u003c\/p\u003e\n\u003cp\u003e7. Data Security\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eData Encoding\u003c\/li\u003e\n\u003cli\u003eData Access\u003c\/li\u003e\n\u003cli\u003eData Secrecy\u003c\/li\u003e\n\u003cli\u003eData Replication\u003c\/li\u003e\n\u003cli\u003eData Backup\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection B. AI Solution Development Methodologies and Lifecycle\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. AI Solution Development Life Cycle\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUse Case Development\u003c\/li\u003e\n\u003cli\u003eDesign\u003c\/li\u003e\n\u003cli\u003eDevelopment\u003c\/li\u003e\n\u003cli\u003eDeployment\u003c\/li\u003e\n\u003cli\u003eMonitoring and Maintenance\u003c\/li\u003e\n\u003cli\u003eDecommission\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Privacy and Security by Design\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplainability\u003c\/li\u003e\n\u003cli\u003eRobustness\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection C. Change Management Specific to AI\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Change Management Considerations\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eData Dependency\u003c\/li\u003e\n\u003cli\u003eAI Model\u003c\/li\u003e\n\u003cli\u003eRegulatory and Societal Impact\u003c\/li\u003e\n\u003cli\u003eEmergency Changes\u003c\/li\u003e\n\u003cli\u003eConfiguration Management\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSection D. Supervision of AI Solutions\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. AI Agency\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eLogging and Monitoring\u003c\/li\u003e\n\u003cli\u003eAI Observability\u003c\/li\u003e\n\u003cli\u003eHuman in the Loop (HITL)\u003c\/li\u003e\n\u003cli\u003eHallucination\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSection E. Testing Techniques for AI Solutions\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Conventional Software Testing Techniques\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eA\/B Testing\u003c\/li\u003e\n\u003cli\u003eUnit and Integration Testing\u003c\/li\u003e\n\u003cli\u003eObjective Verification\u003c\/li\u003e\n\u003cli\u003eCode Reviews\u003c\/li\u003e\n\u003cli\u003eBlack Box Testing\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. AI-Specific Testing Techniques\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel Cards\u003c\/li\u003e\n\u003cli\u003eBias Testing\u003c\/li\u003e\n\u003cli\u003eAdversarial Testing\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection F. Threats and Vulnerabilities Specific to AI\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Types of AI-related Threats\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eTraining Data Leakage\u003c\/li\u003e\n\u003cli\u003eData Poisoning\u003c\/li\u003e\n\u003cli\u003eModel Poisoning\u003c\/li\u003e\n\u003cli\u003eModel Theft\u003c\/li\u003e\n\u003cli\u003ePrompt Injections\u003c\/li\u003e\n\u003cli\u003eModel Evasion\u003c\/li\u003e\n\u003cli\u003eModel Inversion\u003c\/li\u003e\n\u003cli\u003eThreats for Using Vendor Supplied AI\u003c\/li\u003e\n\u003cli\u003eAI Solution Disruption\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Controls for AI-related Threats\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThreat and Vulnerability Identification\u003c\/li\u003e\n\u003cli\u003ePrompt Templates\u003c\/li\u003e\n\u003cli\u003eDefensive Distillation\u003c\/li\u003e\n\u003cli\u003eRegularization\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection G. Incident Response Management Specific to AI\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Prepare\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003ePolicies, Procedures, and Model Documentation\u003c\/li\u003e\n\u003cli\u003eIncident Response Team\u003c\/li\u003e\n\u003cli\u003eTabletop Exercises\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Identify and Report\u003c\/p\u003e\n\u003cp\u003e3. Assess\u003c\/p\u003e\n\u003cp\u003e4. Respond\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eContainment\u003c\/li\u003e\n\u003cli\u003eEradication\u003c\/li\u003e\n\u003cli\u003eRecovery\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e5. Post-Incident Review\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDomain 3. AI Auditing Tools and Techniques\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eLearning Objectives:\u003c\/p\u003e\n\u003cp\u003eWithin this domain, the AI auditor should be able to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluate impacts, opportunities, and risk when integrating AI solutions within the audit process.\u003c\/li\u003e\n\u003cli\u003eUtilize AI solutions to enhance audit processes, including planning, execution, and reporting.\u003c\/li\u003e\n\u003cli\u003eEvaluate the monitoring and reporting of metrics (e.g., KPIs, KRIs) specific to AI.\u003c\/li\u003e\n\u003cli\u003eEvaluate data input requirements for AI models (e.g., data appropriateness, bias, and privacy).\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection A. Audit Planning and Design\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Identification of AI Assets and Controls\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eInventory Objective and Procedure\u003c\/li\u003e\n\u003cli\u003eInventory and Data Gathering Methods\u003c\/li\u003e\n\u003cli\u003eDocumentation\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\n\u003cli\u003eSurveys\u003c\/li\u003e\n\u003cli\u003eInterviews\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Types of AI Controls\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExamples including Control Categories, Controls, and Explanations\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e3. Audit Use Cases\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eLarge Language Models\u003c\/li\u003e\n\u003cli\u003eAudit Process Improvement\u003c\/li\u003e\n\u003cli\u003eGenerative AI\u003c\/li\u003e\n\u003cli\u003eAudit-Specific AI Applications\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e4. Internal Training for AI Use\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eKey Components for Auditor Knowledge\u003c\/li\u003e\n\u003cli\u003ePractical Skills Development\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection B. Audit Testing and Sampling Methodologies\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Designing an AI Audit\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAI Audit Objectives\u003c\/li\u003e\n\u003cli\u003eAudit Scoping and Resources\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. AI Audit Testing Methodologies\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAI Systems Overall Testing\u003c\/li\u003e\n\u003cli\u003eFinancial Models\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e3. AI Sampling\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eJudgmental sampling\u003c\/li\u003e\n\u003cli\u003eAI sampling\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e4. Outcomes of AI testing\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eReduce false positives\u003c\/li\u003e\n\u003cli\u003eReduce workforce needs\u003c\/li\u003e\n\u003cli\u003eOutliers\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection C. Audit Evidence Collection Techniques\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Data Collection\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eTraining and Testing Data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\n\u003cli\u003eUnstructured and Structured Data Collection\u003c\/li\u003e\n\u003cli\u003eExtract, Transform, and Load\u003c\/li\u003e\n\u003cli\u003eData Manipulation\u003c\/li\u003e\n\u003cli\u003eScraping\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Walkthroughs and interviews\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDesign Interview Questions\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e3. AI Collection Tools\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUsing AI to Collect Logs\u003c\/li\u003e\n\u003cli\u003eAI agents to create outputs\u003c\/li\u003e\n\u003cli\u003eVoice to Speech\u003c\/li\u003e\n\u003cli\u003eOptimal Character Recognition\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection D. Audit Data Quality and Data Analytics\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Data Quality\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimization\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Data Analytics\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eSentiment Analysis\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\n\u003cli\u003eRun Data Analytics\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e3. Data Reporting\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eReports\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\n\u003cli\u003eDashboards\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSection E. AI Audit Outputs and Reports\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e1. Reports\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eReport Types (examples and details)\u003c\/li\u003e\n\u003cli\u003eAdvisory Reports\u003c\/li\u003e\n\u003cli\u003eCharts and Visualizations\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e2. Audit Follow-up\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAutomated follow-up\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e3. Quality Assurance and mitigate risk.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"267A85US \/ 2026-07-13T09:00:00 \/ Online","offer_id":47534214512859,"sku":"US-2020-IL","price":2396.0,"currency_code":"USD","in_stock":true},{"title":"26AB40US \/ 2026-10-13T09:00:00 \/ Herndon, VA","offer_id":48216540643547,"sku":"US-2020-IL","price":2396.0,"currency_code":"USD","in_stock":true},{"title":"271B58US \/ 2027-01-11T09:00:00 \/ Herndon, VA","offer_id":48216540676315,"sku":"US-2020-IL","price":2396.0,"currency_code":"USD","in_stock":true},{"title":"274B26US \/ 2027-04-12T09:00:00 \/ Herndon, VA","offer_id":48586417275099,"sku":"US-2020-IL","price":2396.0,"currency_code":"USD","in_stock":true}],"url":"https:\/\/learningtreeinternational-dirinfosec-hhs.myshopify.com\/products\/isaca-advanced-in-ai-audit-aaia-certification","provider":"Learning Tree International","version":"1.0","type":"link"}