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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 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 3
  • 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q155-Q160):

NEW QUESTION # 155
Which of the following BEST describes an adversarial attack on an AI model?

Answer: A

Explanation:
In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged. Hardware compromise (A) and DoS (D) are system-level attacks, not adversarial ML per se. Social engineering to obtain model information (C) is an information-gathering or security breach vector, distinct from input-space adversarial manipulation.
References:AI Security Management (AAISM) Body of Knowledge: Taxonomy of AI Threats- Adversarial Examples and Evasion; Distinction from System/Network Attacks.AAISM Study Guide:
Definitions and Threat Models in Adversarial ML; Attack-Defense Mapping for Model Predictions.


NEW QUESTION # 156
A healthcare provider uses a third-party large language model (LLM) to summarize patient records. A risk analysis indicates a high likelihood of potential data leakage in excess of the organization's risk tolerance. Which of the following is the BEST course of action taken by the vendor to reduce risk to an acceptable level?

Answer: A

Explanation:
Migrating the LLM workload to a private instance and restricting access most effectively reduces the risk of sensitive patient data leakage. This approach provides stronger control over data handling, processing environments, access management, and exposure pathways, making it the most appropriate risk reduction measure for healthcare data.


NEW QUESTION # 157
Which of the following controls is MOST important in preventing malicious actors from biasing model output?

Answer: A

Explanation:
Input validation is the most important control because malicious actors often attempt to bias model outputs through manipulated, malformed, or adversarial inputs. Proper validation helps detect and block harmful inputs before they influence the AI model's behavior or responses.


NEW QUESTION # 158
During the creation of a new large language model (LLM), an organization procured training data from multiple sources. Which of the following is MOST likely to address the CISO's security and privacy concerns?

Answer: D

Explanation:
AAISM guidance highlights data minimization as a critical practice for addressing both security and privacy concerns. By ensuring that only the minimum necessary data is collected and retained, the organization reduces the risk of sensitive information being exposed or misused during training. Data augmentation expands data but does not mitigate privacy risk. Classification organizes data but does not limit exposure.
Data discovery helps locate sources but does not directly reduce risks. The control that directly aligns with privacy-by-design principles is data minimization.
References:
AAISM Exam Content Outline - AI Risk Management (Data Privacy and Minimization) AI Security Management Study Guide - Privacy Safeguards in AI Training


NEW QUESTION # 159
Which of the following AI-driven systems should have the MOST stringent recovery time objective (RTO)?

Answer: D

Explanation:
AAISM risk guidance notes that the most stringent recovery objectives apply to industrial control systems, as downtime can directly disrupt critical infrastructure, manufacturing, or safety operations. Health support systems also require high availability, but industrial control often underpins safety-critical and real-time environments where delays can result in catastrophic outcomes. Credit risk models and navigation systems are important but less critical in terms of immediate physical and operational impact. Thus, industrial control systems require the tightest RTO.


NEW QUESTION # 160
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