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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Management | 31% | - AI risk assessment, thresholds, and treatment strategies - AI vendor and supply chain risk management - Regulatory compliance and ethical considerations in AI - AI threat and vulnerability management |
| AI Governance and Program Management | 31% | - AI asset and data lifecycle management - AI governance frameworks and alignment with business objectives - AI security policies, standards, and procedures - AI security program development and management - Business continuity and incident response for AI systems |
| AI Technologies and Controls | 38% | - AI security architecture and secure design principles - Security monitoring, testing, and continuous assurance for AI systems - Data management and protection controls for AI - Privacy, ethics, trust, and safety controls - AI lifecycle security: model selection, training, validation, and deployment |
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質問 # 376
An incident response team has restored an AI system after a confirmed security incident. Which of the following should be done NEXT?
正解：B
解説：
After restoration, the immediate next step is to ensure the AI system is functioning correctly and producing expected, reliable outputs. This confirms that the system has been fully and safely recovered before moving on to communication or further analysis.
質問 # 377
Which of the following BEST reduces the risk of exposing sensitive data through the output of large language models (LLMs) in applications?
正解：D
解説：
AAISM materials make clear that the best safeguard against sensitive information being leaked through the outputs of LLMs is data sanitization. This involves filtering, redacting, or masking sensitive content before the model can use it, thereby preventing unintended disclosure in outputs. Encryption protects confidentiality in storage and transmission but does not stop output leaks. Adversarial testing helps identify vulnerabilities but does not prevent exposure by itself. Least privilege access restricts who can interact with the model but does not sanitize the content of its outputs. The control most directly tied to preventing leakage is implementing data sanitization techniques.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Data Leakage Prevention) AI Security Management Study Guide - Sensitive Data Controls in Generative AI
質問 # 378
Which of the following would MOST effectively ensure an organization developing AI systems has comprehensive data classification and inventory management?
正解：C
解説：
AAISM governance practices emphasize automation as the most effective way to maintain comprehensive, accurate, and scalable data classification and inventory management. An automated data cataloging tool integrated with all repositories ensures continuous visibility, reduces human error, and supports regulatory compliance. Centralized teams and manual processes provide oversight but cannot achieve the same scale and consistency. Periodic audits detect issues but are reactive rather than proactive. For effective governance, the best solution is automated cataloging integrated across data repositories.
質問 # 379
An organization plans to use an open-source foundational AI model. Which of the following is MOST important for the AI governance committee to consider when approving its use?
正解：C
解説：
AAISM emphasizes that open-source AI models present elevated data leakage risks because internal data may flow into external, uncontrolled repositories or be used for further training. Governance bodies must prioritize the risk of data exposure, model reuse, data retention uncertainty, and uncontrolled model behavior.
While accuracy (B) and support (C) are important operational considerations, they are not the primary governance risk. Employee privacy rights (D) matter but are encompassed within the broader risk of data leakage.
References: AAISM Study Guide - Open-Source AI Governance; Data Leakage and Uncontrolled Model Behavior.
質問 # 380
Which of the following BEST describes how supervised learning models help reduce false positives in cybersecurity threat detection?
正解：A
解説：
According to AAISM technical content, supervised learning models reduce false positives by learning from historical labeled data that distinguishes between legitimate activity and actual threats. This training enables the model to recognize patterns and improve its discrimination ability over time. Grouping patterns (A) describes clustering, an unsupervised method. Real-time feature engineering (B) and generating new labeled data (D) are advanced techniques but not the fundamental supervised learning approach. The essence of supervised learning is leveraging labeled data to minimize misclassification, including false positives.
質問 # 381
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