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Title: Free PDF Quiz ISACA - Valid AAISM Test Discount Voucher [Print This Page]
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Title: Free PDF Quiz ISACA - Valid AAISM Test Discount Voucher
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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 (Q151-Q156):NEW QUESTION # 151
When robust input controls are not practical on a large language model (LLM) to prevent prompt injection attacks from external threats, which of the following would be the BEST compensating control to address the risk?
Answer: D
Explanation:
When preventive input hardening isn't feasible for LLMs, AAISM prescribes compensating detective and corrective controls-notably human review and annotation of outputs prior to downstream action-to reduce harm from prompt injection. Output-side review gates prevent untrusted instructions from propagating, enable rapid suppression/feedback loops, and provide labeled examples for subsequent model hardening. IAM (B) is necessary but does not mitigate injection in content; reviewing inputs (C) is less effective than auditing what the model is about to act on; fine-tuning for validation (D) is helpful long-term but is not an immediate compensating control when robust input validation is impractical.
References: AI Security Management (AAISM) Body of Knowledge - LLM Threats & Compensating Controls; Human Oversight & Output Review Gates; Post-incident Feedback and Labeling for Model Hardening.

NEW QUESTION # 152
Which of the following BEST describes the role of model cards in AI solutions?
Answer: C
Explanation:
AAISM positions model cards as standardized documentation artifacts that record intended use and out-of- scope use, training/evaluation data characteristics, performance metrics across groups, limitations/risks, and governance controls/owners. Their purpose is transparency and assurance, not automated tuning or synthetic data generation. Visualization (A) may appear within a card, but the core role is structured documentation for governance, risk, and compliance.
References: AI Security Management™ (AAISM) Body of Knowledge - Documentation & Transparency Artifacts; Model Cards for Governance, Risk, and Assurance; Intended Use, Limitations, and Performance Disclosure.

NEW QUESTION # 153
An organization is implementing AI agent development across engineering teams. What should AI-specific training focus on?
Answer: A
Explanation:
AAISM states that AI agent security training should focus on the unique risks of agentic systems, which include:
* prompt injection
* memory control and context hijacking
* unsafe tool execution (agents triggering unauthorized actions)
These risks are specific to autonomous or semi-autonomous AI agents.
Bias, fairness (B) and output moderation (C) are important but not the most critical for agent security. API abuse and plug-in risk (D) matter but are secondary.
References: AAISM Study Guide - Agentic AI Security; Prompt Injection and Tool Execution Risks.

NEW QUESTION # 154
An organization decides to use an anomaly-based intrusion detection system (IDS) integrated with a generative adversarial network (GAN)-enabled AI tool. The integrated tool would MOST effectively detect intrusions by leveraging:
Answer: D
Explanation:
AAISM materials describe that GAN-based systems excel at generating synthetic data-including simulated attack traffic-which can significantly enhance anomaly-based intrusion detection capabilities. The guidance emphasizes that synthetic attack samples help strengthen the model's ability to detect rare or emerging intrusion types. This aligns with the principle that AI security controls should leverage adversarially generated data during training to improve resilience.
Options A and C describe generic ML enhancements, but not GAN-specific advantages. Option B is useful but insufficient for anomaly detection, which relies heavily on recognizing atypical, previously unseen patterns.
References: AAISM Study Guide - AI Security Controls; Adversarial Simulation and Synthetic Data Generation.

NEW QUESTION # 155
Which of the following BEST ensures the integrity of data sets used to train AI models?
Answer: A
Explanation:
AAISM defines cryptographic tracking and verification as the best control for ensuring the integrity of training data. By applying hashing and verification methods, organizations can confirm that datasets remain unaltered and authentic throughout collection, storage, and processing. Collecting only necessary data, proper storage, or clear documentation all support governance and compliance, but they do not guarantee that the data has not been tampered with. Integrity is specifically ensured by cryptographic verification techniques.
References:
AAISM Exam Content Outline - AI Risk Management (Data Integrity and Protection) AI Security Management Study Guide - Cryptographic Controls for Dataset Integrity

NEW QUESTION # 156
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