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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q240-Q245):

NEW QUESTION # 240
Which of the following would BEST protect trade secrets related to AI technologies during their life cycle?

Answer: C

Explanation:
Restricting access to sensitive data and artifacts (e.g., training data, feature stores, model weights, prompts, system designs) using least-privilege, segregation, encryption, and monitoring is the most effective way to protect trade secrets throughout the AI lifecycle. Patents require public disclosure, trademarks protect branding (not secrets), and output watermarks help provenance/abuse deterrence but do not secure underlying proprietary know-how.
References: AI Security Management (AAISM) Body of Knowledge: Information Protection for AI- Access Control, Segmentation, and Secrets Management; AAISM Study Guide: Lifecycle Security of AI Artifacts and Trade-Secret Safeguards.


NEW QUESTION # 241
When generative AI output data is developed by a third party, which of the following MUST be in place to protect against code-level vulnerabilities?

Answer: A

Explanation:
Robust third-party contracts establish security requirements, responsibilities, and accountability for addressing code-level vulnerabilities, ensuring the vendor implements appropriate controls and remediation processes.


NEW QUESTION # 242
A preliminary risk assessment of a SaaS-based large language model (LLM) business support system has identified prompt injection, data poisoning, and model exfiltration as material threats. Which of the following is the BEST approach to ensure risks are treated consistently?

Answer: D

Explanation:
AAISM prescribes building and maintaining an AI Threat-Control Matrix to ensure consistent, repeatable risk treatment. The matrix traces each material threat (e.g., prompt injection, poisoning, exfiltration) to named controls, test/evidence procedures, and assurance owners across the lifecycle. Baselines and vendor attestations can support assurance but are insufficient alone; deferring control selection until after deployment conflicts with AAISM's proactive treatment principle.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Treatment Planning; Threat-Control Traceability; Assurance & Evidence Management for AI Systems.


NEW QUESTION # 243
An aerospace manufacturing company that prioritizes accuracy and security has decided to use generative AI to enhance operations. Which of the following large language model (LLM) adoption plans BEST aligns with the company's risk appetite?

Answer: B

Explanation:
AAISM recommends aligning AI adoption with organizational risk appetite by limiting blast radius, protecting sensitive data, and staging adoption in lower-risk domains first. Building a private LLM for non-critical functions preserves data control, enables tighter governance (access control, logging, evaluation), and confines any model errors away from safety- or mission-critical operations. A public LLM for critical functions (A) is misaligned with a high-assurance posture; buying open-market datasets (B) raises provenance and licensing risk; third-party access (C) can be appropriate but still introduces vendor/visibility limits and data residency concerns that may not meet aerospace security needs.
References: AI Security Management (AAISM) Body of Knowledge - Risk Appetite Mapping to AI Use Cases; Criticality Segmentation; Data Control & Deployment Models. AAISM Study Guide - Phased Adoption for High-Assurance Environments; Private vs. Hosted LLM Trade-offs; Governance, Evaluation, and Containment Patterns.


NEW QUESTION # 244
Which of the following AI controls would BEST protect against deepfake content?

Answer: C

Explanation:
Data provenance establishes the origin and authenticity of content, enabling verification of whether media has been altered or fabricated, which is essential for detecting and protecting against deepfakes.


NEW QUESTION # 245
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