AAISM Valid Test Questions & AAISM Mock Test

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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 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 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 (Q327-Q332):

NEW QUESTION # 327
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?

Answer: A

Explanation:
AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems.
A certificate of destruction ensures:
- proof of lawful data disposal
- auditability
- regulatory compliance
- defensibility during inspections


NEW QUESTION # 328
Which of the following is the BEST mitigation control for membership inference attacks on AI systems?

Answer: C

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 # 329
A large corporation has received an influx of sophisticated credential-phishing emails and wants to leverage an AI solution to detect and quarantine these messages before they reach employees. Which of the following blue-team AI features is BEST suited to this task?

Answer: A

Explanation:
For pre-delivery phishing detection and classification, the most appropriate capability is NLP- tokenization, feature extraction, semantic similarity, and supervised classifiers (e.g., transformer-based classifiers) tuned on phishing corpora and indicators. NLP models score messages and drive automated quarantine policies. An LLM (Option A) is a model type, not a specific blue-team feature; NLG (Option C) is for generation, not detection; RAG (Option D) augments responses with retrieved knowledge but does not by itself optimize classification and quarantine of phishing emails.
References:
AAISM Body of Knowledge: Defensive AI Use Cases; Text Classification Pipelines for Security Operations; Email Security and AI-Driven Triage.
AAISM Study Guide: NLP for Threat Detection; Model Evaluation for Precision/Recall in Security Classifiers; SOC Integration and Automated Containment.


NEW QUESTION # 330
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.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Model Provenance and Watermarking) AI Security Management Study Guide - Ownership and Accountability of Models


NEW QUESTION # 331
A critical AI system shows biased outcomes. What is the BEST course of action?

Answer: D

Explanation:
AAISM emphasizes root cause analysis as the primary step when AI behavior deviates unexpectedly.
RCA determines whether issues stem from:
- data drift
- model drift
- poisoned data
- missing features
- logic errors
- configuration changes


NEW QUESTION # 332
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