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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Management | 31% | - AI risk assessment, thresholds, and treatment strategies - Regulatory compliance and ethical considerations in AI - AI threat and vulnerability management - AI vendor and supply chain risk management |
| Topic 2: AI Technologies and Controls | 38% | - AI security architecture and secure design principles - AI lifecycle security: model selection, training, validation, and deployment - Data management and protection controls for AI - Privacy, ethics, trust, and safety controls - Security monitoring, testing, and continuous assurance for AI systems |
| Topic 3: AI Governance and Program Management | 31% | - Business continuity and incident response for AI systems - AI security policies, standards, and procedures - AI asset and data lifecycle management - AI governance frameworks and alignment with business objectives - AI security program development and management |
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NEW QUESTION # 61
Which of the following is the BEST source of information for AI developers to review when selecting an appropriate machine learning (ML) model and identifying key risk?
Answer: C
Explanation:
AI model cards provide structured information about a machine learning model's intended use, performance characteristics, limitations, risks, ethical considerations, and testing results. This makes them the best source for developers when selecting appropriate models and identifying associated risks.
NEW QUESTION # 62
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?
Answer: D
Explanation:
AAISM guidance clearly states that the most effective way to mitigate data bias is through diverse training data that fairly represents all relevant populations, scenarios, and contexts. Simplified models may reduce complexity but do not remove bias. Unstructured data sets may introduce new errors without addressing fairness. Securing training data protects confidentiality and integrity but does not resolve representational imbalance. Therefore, the best practice for reducing bias in ML is diversification of training datasets.
References:
AAISM Study Guide - AI Risk Management (Bias and Fairness in AI)
ISACA AI Security Management - Data Diversity and Representation Controls
NEW QUESTION # 63
A CISO must provide KPIs for the organization's newly deployed AI chatbot. Which metrics are BEST?
Answer: C
Explanation:
AAISM recommends that AI KPIs should emphasize:
* Error rates - measure correctness and reliability
* Bias detection metrics - assess fairness and harm risk
These are the core governance KPIs for AI systems interacting with end users.
Response time (A) is a performance metric but not an AI governance KPI. Customer retention (C) is business- focused. F1 score (D) is useful but not as critical as bias monitoring.
References: AAISM Study Guide - AI Performance, Fairness & Governance Metrics.
NEW QUESTION # 64
The PRIMARY objective of performing tests to detect membership inference in machine learning (ML) models is to determine whether the model:
Answer: C
Explanation:
Membership inference testing evaluates whether a model can disclose whether specific data points were part of its training dataset, indicating potential leakage of sensitive information about the data it was trained on.
NEW QUESTION # 65
Which of the following methods provides the MOST effective protection against model inversion attacks?
Answer: A
Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.
* A (adversarial training) targets perturbation robustness, not primary for inversion.
* B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.
* D (more iterations) typically increases overfitting and leakage risk.
AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Security-Privacy leakage threats (membership inference, inversion) and mitigation via regularization and output minimization.* AI Security Management™ Study Guide: Overfitting controls, calibration and confidence suppression as defenses against inference attacks.
NEW QUESTION # 66
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