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[Hardware] Study AAISM Tool - Latest AAISM Exam Notes

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【Hardware】 Study AAISM Tool - Latest AAISM Exam Notes

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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 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 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q162-Q167):NEW QUESTION # 162
Which of the following reviews MUST be conducted as part of an AI impact assessment?
  • A. Evaluation of model reproducibility
  • B. Identification of environmental and societal consequences
  • C. Testing, evaluation, validation, and verification
  • D. Security control self-assessment (CSA)
Answer: B
Explanation:
The AAISM material frames an AI impact assessment as broader than a technical model review. It emphasizes that governance of AI includes assessing societal, environmental, and human-rights impacts in addition to organizational risk. The official guidance notes that high-level impact assessments must explicitly consider "potential consequences for individuals, communities, and the environment" as part of responsible AI governance. Activities such as testing/validation (A) and reproducibility (B) are critical in the development lifecycle but are not, by themselves, sufficient to constitute an impact assessment. A CSA (C) is focused on security controls, which is narrower than overall impact. The mandatory element that differentiates an AI impact assessment is the assessment of environmental and societal consequences, making option D the correct answer in alignment with governance expectations.
References: AI Security Management™ (AAISM) Study Guide - AI Governance and Impact Assessments; Responsible and Trustworthy AI Section.

NEW QUESTION # 163
Which of the following types of testing can MOST effectively mitigate prompt hacking?
  • A. Regression
  • B. Adversarial
  • C. Input
  • D. Load
Answer: B
Explanation:
Prompt hacking manipulates large language models by injecting adversarial instructions into inputs to bypass or override safeguards. The AAISM framework identifies adversarial testing as the most effective way to simulate such manipulative attempts, expose vulnerabilities, and improve the resilience of controls. Load testing evaluates performance, input testing checks format validation, and regression testing validates functionality after changes. None of these directly address the manipulation of natural language inputs.
Adversarial testing is therefore the correct approach to mitigate prompt hacking risks.
References:
AAISM Exam Content Outline - AI Risk Management (Testing and Assurance Practices) AI Security Management Study Guide - Adversarial Testing Against Prompt Manipulation

NEW QUESTION # 164
Which of the following is the BEST mitigation control for membership inference attacks on AI systems?
  • A. Model ensemble techniques
  • B. Differential privacy
  • C. Cybersecurity-oriented red teaming
  • D. AI threat modeling
Answer: B
Explanation:
Membership inference attacks attempt to determine whether a particular data point was part of a model's training set, which risks violating privacy. The AAISM study guide highlights differential privacy as the most effective mitigation because it introduces mathematical noise that obscures individual contributions without significantly degrading model performance. Ensemble methods improve robustness but do not specifically protect privacy. Threat modeling and red teaming help identify risks but are not direct controls. The explicit mitigation control aligned with privacy preservation for membership inference is differential privacy.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Techniques) ISACA AI Security Management - Membership Inference Mitigations

NEW QUESTION # 165
An AI system that supports critical processes has deviated from expected performance and is producing biased outcomes. Which of the following is the BEST course of action?
  • A. Activate the model kill switch
  • B. Conduct audits of the data and the model
  • C. Retrain the model with a new and expanded dataset
  • D. Perform a root cause analysis to identify mitigation steps
Answer: D
Explanation:
AAISM directs that when harmful or biased behavior is observed in a production AI system, the organization should enter a formal incident/variance handling workflow that begins with root cause analysis (RCA) to identify the source of deviation (data drift, concept drift, feature leakage, pipeline changes, control failures) and determine proportionate risk treatments. Immediate retraining (Option A) without RCA risks reinforcing the same bias; audits (Option C) are key activities within RCA rather than the action that frames the response; a kill switch (Option D) is reserved for conditions where risk exceeds the defined tolerances and immediate harm prevention is required.
References: AI Security Management (AAISM) Body of Knowledge - Incident Response & Post-Incident Improvement; Model Risk Treatment & Drift Management; Bias Detection and Remediation Governance.

NEW QUESTION # 166
The PRIMARY ethical concern of generative AI is that it may:
  • A. Breach the confidentiality of information
  • B. Cause information to become unavailable
  • C. Cause information integrity issues
  • D. Produce unexpected data that could lead to bias
Answer: C
Explanation:
AAISM materials emphasize that the primary ethical concern with generative AI is the risk to information integrity. Generative models can create content that appears authentic but is fabricated, misleading, or manipulated. This undermines trust in information ecosystems and can have wide-reaching social, legal, and organizational impacts. While confidentiality breaches and bias are concerns, they are not the central ethical issue inherent to generative models. Availability is less relevant in this context. The most pressing concern is that generative AI may compromise the integrity of information.
References:
AAISM Study Guide - AI Risk Management (Ethical Risks of Generative AI) ISACA AI Security Management - Integrity Concerns in Generative Systems

NEW QUESTION # 167
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