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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 (Q348-Q353):

NEW QUESTION # 348
Embedding unique identifiers into AI models would BEST help with:

Answer: B

Explanation:
The AAISM framework explains that embedding unique identifiers--such as digital watermarks or model fingerprints--enables organizations to trace and verify model provenance. This technique is used for tracking ownership and intellectual property rights over models, particularly when sharing, licensing, or distributing AI systems. While identifiers may support certain security functions, their primary control objective is ownership verification, not preventing access, bias removal, or adversarial detection. The correct alignment with AAISM controls is tracking ownership.


NEW QUESTION # 349
Which of the following is the BEST way to reduce the risk of misuse of an AI agent that has access to critical data and systems?

Answer: C

Explanation:
AAISM frameworks highlight output-based controls, including output filtering, restriction validation, and policy-aligned guardrails as primary defenses for AI agents with high privileges. Ensuring the agent does not output unauthorized instructions or sensitive data directly mitigates misuse.


NEW QUESTION # 350
After implementing a third-party generative AI tool, an organization learns about new regulations related to how organizations use AI. Which of the following would be the BEST justification for the organization to decide not to comply?

Answer: C

Explanation:
The AAISM framework clarifies that compliance decisions must always be tied to an organization's risk appetite and tolerance. When new regulations emerge, management may choose not to comply if the associated risk remains within the documented and approved risk appetite, provided that accountability is established and governance structures support this decision. Other options such as widespread industry use, third-party audits, or lack of cost assessment do not justify noncompliance under the governance principles.
The risk appetite framework is the only recognized justification under AI governance principles.
References:
AAISM Study Guide - AI Governance and Program Management
ISACA AI Risk Guidance - Risk Appetite and Compliance Decisions


NEW QUESTION # 351
A global organization experienced multiple incidents of staff pasting confidential data into public chatbots. Which action is MOST important to reduce short-term risk?

Answer: A

Explanation:
AAISM states that the most effective short-term mitigation for unintentional data leakage into public AI tools is targeted, role-based AI security awareness, focused on:
- what data cannot be entered
- consequences of leakage
- real-world scenarios employees face
An acceptable-use policy (C) is necessary but insufficient alone. Blocking LLMs (D) may reduce access but does not change user behavior and may cause shadow AI usage. Generic phishing training (B) does not address AI misuse risks.


NEW QUESTION # 352
What BEST ensures a proper business continuity plan (BCP) for an AI solution?

Answer: C

Explanation:
AAISM defines an AI BCP as requiring validated failover and recovery testing of AI components, including:
- model hosting environments
- model-serving APIs
- feature stores
- inference pipelines
Only Option B tests actual continuity of AI functionality.


NEW QUESTION # 353
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