ISACA Advanced in AI Audit (AAIA)
- Code training AAIA
- Duur 2 dagen
- Versie 1.0
Andere trainingsmethoden
Ga naar:
Methode
Deze training is in de volgende formats beschikbaar:
-
E-learning (in je eigen tempo)
Elektronisch leren in je eigen tempo
-
Klassikale training
Klassikaal leren
-
Op locatie klant
Op locatie klant
-
Virtueel leren
Virtueel leren
Vraag deze training aan in een andere lesvorm.
Trainingsbeschrijving
Naar bovenPrepare to audit, govern, and manage AI systems with confidence through ISACA’s AAIA certification prep.
The AAIA Certification Prep Course is designed to help professionals build the expertise needed to audit and govern AI systems with confidence. As artificial intelligence becomes integral to business operations, organizations face new challenges around ethics, compliance, and risk. This course provides a practical framework for understanding AI governance, managing risk, and aligning AI initiatives with organizational objectives.
Participants will explore the full AI lifecycle, from data management and model development to security controls and change management. The program covers proven techniques for testing AI systems, identifying vulnerabilities, and responding to incidents. It also offers guidance on planning and conducting AI-focused audits, collecting reliable evidence, and delivering clear, actionable reports.
Whether you’re preparing for ISACA’s AAIA certification or looking to strengthen your ability to oversee AI programs, this course equips you with the tools and knowledge to ensure transparency, accountability, and compliance in an AI-driven world.
Learners will have access to the course for one year from date of purchase and will earn 11 CPE upon completion. This course has a seat time of approximately 11 hours and is accessed via the Learning Access tab of your MyISACA dashboard.
This course uses dynamic modules, with integrated videos, text, and interactive elements to ensure a thorough grasp of all three AAIA domains:
AI Governance and Risk, including risk management, program management, data governance and leading practices, ethics, regulations, and standards for AI.
AI Operations, including data management, solution development, threats and vulnerabilities and change management specific to AI;
AI Auditing Tools and Techniques, including audit planning and design, testing and sampling, evidence collection, audit reporting and data analytics.
As you navigate through the course, you'll engage with a variety of educational tools and can use our structured study plan to guide your preparation to make sure you are ready for exam day.
The course content is equipped with adjustable settings for volume, speed, and quality, accommodating different learning preferences and ensuring optimal clarity. Features like closed captions and a transcript panel are also available to enhance your learning experience.
Doelgroep
Naar bovenThis course is designed for professionals responsible for auditing, governing, or managing AI systems within their organizations, including:
- IT Auditors and Risk Professionals seeking to expand their expertise into AI auditing.
- AI Governance and Compliance Officers tasked with ensuring ethical and regulatory adherence.
- Cybersecurity and Data Privacy Specialists who need to understand AI-specific risks and controls.
- AI Program Managers and Project Leads overseeing AI solution development and lifecycle management.
- Internal and External Auditors who perform audits on AI systems and related processes.
Ideal for individuals preparing for ISACA’s Advanced in AI Audit (AAIA) certification or those looking to strengthen their knowledge of AI governance, risk management, and auditing practices.
Trainingsdoelstellingen
Naar bovenAfter completing this course you should be able to:
- Establish AI Governance and Risk Frameworks
- Assess and Manage AI Risks
- Oversee AI Operations and Development
- Apply AI-Specific Testing and Security Controls
- Conduct AI-Focused Audits
Inhoud training
Naar bovenDomain 1: AI Governance and Risk
AI Models, Considerations and Requirements
- Types of AI
- Machine Learning/AI Models
- Algorithms
- AI Life Cycle
- Business Considerations
AI Governance and Program Management
- AI Strategy
- AI-Related Roles and Responsibilities
- AI-Related Policies and Procedures
- AI Training and Awareness
- Program Metrics
AI Risk Management
- AI-Related Risk Identification
- Risk Assessment
- Risk Monitoring
Privacy and Data Governance Programs
- Data Governance
- Privacy Considerations
Leading Practices, Ethics, Regulations and Standards for AI
- Standards, Frameworks, and Regulations Related to AI
- Ethical Considerations
Domain 2: AI Operations
Data Management Specific to AI
- Data Collection
- Data Classification
- Data Confidentiality
- Data Quality
- Data Balancing
- Data Scarcity
- Data Security
AI Solution Development Methologies and Lifecycle
- AI Solution Development Life Cycle
- Privacy and Security by Design
Change Management Specific to AI
- Change Management Considerations
Supervision of AI Solutions
- AI Agency
Testing Techniques for AI Solutions
- Conventional Software Testing Techniques Applied to AI Solutions
- AI-Specific Testing Techniques
Threats and Vulnerabilities Specific to AI
- Types of AI-Related Threats
- Controls for AI-Related Threats
Incident Response Management Specific to AI
- Prepare
- Identify and Report
- Assess
- Respond
- Post-Incident Review
Domain 3: AI Auditing Tools & Techniques
Audit Planning and Design
- Identification of AI Assets
- Types of AI Controls
- AI Audit Use Cases
- Internal Training for AI Use
Audit Testing and Sampling Methodolgies
- Designing an AI Audit
- AI Audit Testing Methodologies
- AI Sampling Testing
- AI Outcomes Sample
- AI Audit Process
Audit Evidence Collection Techniques
- Data Collection
- Walkthroughs and Interviews
- AI Collection Tools
Audit Data Quality and Data Analytics
- Data Quality
- Data Analytics
- Data Reporting
AI Audit Outputs and Reports
- Reports
- Audit Follow-up
- Quality Assurance
Voorkennis
Naar bovenIt is recommended that you have the following prerequisites:
- A solid understanding of IT governance, risk management, and compliance frameworks.
- Familiarity with AI concepts and terminology, including machine learning models and data governance.
- Basic knowledge of information security and privacy principles.
- Experience with audit processes and methodologies in a technology environment.
- ISACA CISA Certification (Certified Information Systems Auditor) or equivalent auditing experience.
Examen
Naar bovenRecommended as preparation for the following exams:
- AAIA - ISACA - Advanced in AI Audit