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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance and Program Management | 31% | - AI Strategy and Policy
|
| Topic 2: AI Technologies and Controls | 38% | - Data and Model Controls
|
| Topic 3: AI Risk Management | 31% | - AI Risk Assessment
|
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NEW QUESTION # 319
Which of the following is the MOST effective use of AI-enabled tools in a security operations center (SOC)?
Answer: B
Explanation:
The most effective SOC application of AI is in detecting subtle, hard-to-find attack patterns that reduce false negatives.
AAISM technical control guidance notes that AI in SOCs is best applied to:
* Enhance detection accuracy and sensitivity to anomalies.
* Assist analysts in identifying hidden patterns that traditional rule-based systems miss.
* Augment--not replace--human decision-making for high-confidence outcomes.
NEW QUESTION # 320
When creating a use case for an AI model that provides sensitive decisions affecting end users, which of the following is the GREATEST benefit of using model cards?
Answer: D
Explanation:
AAISM highlights that model cards are a governance tool designed to document ethical considerations, limitations, fairness constraints, data sources, and suitability of use cases for AI models-especially when they affect individuals' rights, opportunities, or access to services.
Their greatest value is providing transparency and ethical clarity, ensuring stakeholders understand risks, bias considerations, and how decisions impact users.
Deployment instructions (B) are not part of model cards. They do not reduce data needs (C), nor is model type selection (D) their primary purpose.
References: AAISM Study Guide - AI Governance Documentation; Transparency and Model Cards.
NEW QUESTION # 321
During red-team testing of an AI system used to make lending decisions, which of the following techniques BEST simulates a data poisoning attack?
Answer: A
Explanation:
AAISM defines data poisoning as the intentional manipulation of training data so that the learned model behaves incorrectly (e.g., skewed lending approvals/denials) while appearing valid. The correct simulation in red-team exercises is to corrupt or seed the training set with adversarial examples or mislabeled records to induce biased or erroneous decision boundaries. Encrypting inputs (A) is unrelated; output noise (B) describes perturbation of predictions, not training; model weight theft (C) is model extraction, not poisoning.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML Threats; Data Poisoning and Training-Time Attacks. AAISM Study Guide - Red-Team Methods for AI; Poisoning vs.
Evasion vs. Model Extraction; Controls and Testing for Safety-Critical Decisions.
NEW QUESTION # 322
An organization is designing an AI-based credit risk assessment system integrating sensitive financial data. Which option BEST supports security-by-design?
Answer: B
Explanation:
AAISM identifies AI-specific threat modeling as an essential early-stage control in security-by- design, particularly for high-risk systems like credit scoring. It systematically identifies:
- data poisoning
- bias vulnerabilities
- model evasion
- model extraction
- misuse scenarios
NEW QUESTION # 323
A malicious actor is able to retrieve the original data that was used to train an AI model through supplying inputs and observing the corresponding outputs of the model. Which of the following BEST describes this type of attack?
Answer: D
Explanation:
A model inversion attack occurs when an attacker reconstructs or retrieves sensitive training data by analyzing the outputs generated from carefully crafted inputs to the AI model. The attack exploits information leakage from the model to infer original training data.
NEW QUESTION # 324
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