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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Technologies and Controls | 38% | - Data management and protection controls for AI - AI security architecture and secure design principles - AI lifecycle security: model selection, training, validation, and deployment - Privacy, ethics, trust, and safety controls - Security monitoring, testing, and continuous assurance for AI systems |
| Topic 2: AI Governance and Program Management | 31% | - AI asset and data lifecycle management - AI security program development and management - AI governance frameworks and alignment with business objectives - AI security policies, standards, and procedures - Business continuity and incident response for AI systems |
| Topic 3: AI Risk Management | 31% | - Regulatory compliance and ethical considerations in AI - AI vendor and supply chain risk management - AI risk assessment, thresholds, and treatment strategies - AI threat and vulnerability management |
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NEW QUESTION # 48
What BEST protects trade secrets related to AI technologies during their life cycle?
Answer: B
Explanation:
AAISM emphasizes access control and data security as the strongest mechanisms to protect trade secrets, including:
* proprietary algorithms
* training datasets
* model weights
* internal design documentation
Trademarks (A) protect brand, not trade secrets. Patents (C) require public disclosure. Watermarks (D) protect generated content, not internal trade secrets.
References: AAISM Study Guide - AI Intellectual Property Protection & Access Controls.
NEW QUESTION # 49
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?
Answer: D
Explanation:
AAISM's approach to third-party and vendor risk for AI systems stresses data usage transparency as a primary control. The guidance explains that organizations must obtain clear documentation on "what data is collected, how it is processed, stored, retained, and whether it is reused for training or shared with other parties." Option C directly addresses this by requiring the vendor to disclose how the application uses organizational data, enabling appropriate risk assessment, contractual controls, and technical safeguards. An external audit (A) can be useful but may be costly and not always feasible pre-procurement. Legal discussions (B) are important but ineffective without clarity on data flows. Publicly available policies (D) are often high- level and marketing-oriented, lacking the specificity required for proper risk evaluation. Therefore, obtaining explicit data usage disclosures from the vendor is the most effective starting point.
References: AI Security Management™ (AAISM) Study Guide - Third-Party AI Risk and Data Sharing; Vendor Governance Requirements.
NEW QUESTION # 50
An organization is planning to commission a third-party AI system to make decisions using sensitive data.
Which of the following metrics is MOST important for the organization to consider?
Answer: D
Explanation:
When AI systems make consequential decisions over sensitive data, AAISM requires explicit performance thresholds tied to decision quality-i.e., accuracy (and related error/false-rate limits) aligned to business risk appetite and regulatory expectations. Availability and latency are important service metrics, but decision integrity and error bounds are primary risk drivers in sensitive contexts. Establishing, monitoring, and enforcing minimum accuracy thresholds (with subgroup performance checks) is essential to reduce harm, ensure fairness/compliance, and support auditability.
References:* AI Security Management (AAISM) Body of Knowledge: Risk-aligned performance metrics; decision quality thresholds; harm and error-rate governance in sensitive processing.* AI Security Management Study Guide: Metric selection for high-risk AI; accuracy, false positive/negative limits, and acceptance criteria tied to business controls.
NEW QUESTION # 51
Which of the following reviews MUST be conducted as part of an AI impact assessment?
Answer: C
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.
NEW QUESTION # 52
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: D
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 # 53
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