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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 2
  • AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 3
  • AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q121-Q126):

NEW QUESTION # 121
A third-party provider is proposing to implement an AI algorithm for access control monitoring to enhance the efficiency of the security control process. Which of the following would be the GREATEST concern for the provider?

Answer: D

Explanation:
AI-driven access control monitoring can produce variable and less predictable performance outcomes compared to traditional deterministic systems. This creates significant concern for the provider because service level agreements may need revision if AI behavior affects reliability, accuracy, or response expectations.


NEW QUESTION # 122
A retail organization implements an AI-driven recommendation system that utilizes customer purchase history. Which of the following is the BEST way for the organization to ensure privacy and comply with regulatory standards?

Answer: D

Explanation:
According to the AI Security ManagementTM (AAISM) study framework, compliance with privacy and regulatory standards must begin with a formalized process of identifying, documenting, and maintaining applicable obligations. The guidance explicitly notes that organizations should maintain a comprehensive register of legal and regulatory requirements to ensure accountability and alignment with privacy laws. This register serves as the foundation for all governance, risk, and control practices surrounding AI systems that handle personal data.
Maintaining such a register ensures that the recommendation system operates under the principles of privacy by design and privacy by default. It allows decision-makers and auditors to trace every AI data processing activity back to relevant compliance obligations, thereby demonstrating adherence to laws such as GDPR, CCPA, or other jurisdictional mandates.


NEW QUESTION # 123
Which of the following controls BEST builds user trust in AI application output?

Answer: D

Explanation:
Displaying model reasoning helps users understand how conclusions are reached, increasing transparency and confidence in the output, which directly builds trust.


NEW QUESTION # 124
Which AI data management technique involves creating validation and test data?

Answer: C

Explanation:
AAISM describes data splitting as the process of dividing datasets into:
* training
* validation
* test sets
This is essential for reducing overfitting and ensuring robust evaluation.
Learning (A) refers to model training. Annotating (D) labels data. Training (C) does not create validation/test data.
References: AAISM Study Guide - AI Data Preparation & Dataset Splitting.


NEW QUESTION # 125
An organization plans to leverage AI in the software development process to speed up coding.
Which of the following should the information security manager do FIRST?

Answer: B

Explanation:
AAISM guidance specifies that before introducing AI into any business or technical workflow, an AI Impact Assessment must be conducted early to determine potential risks, privacy implications, misuse scenarios, governance gaps, and required security controls. This aligns with the principle that AI adoption must begin with governance and risk identification, not training or policy modification.


NEW QUESTION # 126
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