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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance and Program Management | 31% | - Business continuity and incident response for AI systems - AI security policies, standards, and procedures - AI governance frameworks and alignment with business objectives - AI asset and data lifecycle management - AI security program development and management |
| Topic 2: 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 3: AI Risk Management | 31% | - AI threat and vulnerability management - AI vendor and supply chain risk management - Regulatory compliance and ethical considerations in AI - AI risk assessment, thresholds, and treatment strategies |
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NEW QUESTION # 245
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?
Answer: C
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 # 246
Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models used for fraud detection systems?
Answer: A
Explanation:
AAISM defines differential privacy as a technique that ensures individual data points cannot be reverse- engineered or identified, even when used to train high-risk AI models such as fraud detection systems. Its primary purpose is to protect individual privacy while maintaining aggregate utility.
Accuracy improvements (B) or performance optimizations (A, D) are not core objectives of differential privacy.
References: AAISM Study Guide - Privacy-Preserving Machine Learning; Differential Privacy Fundamentals.
NEW QUESTION # 247
Which of the following types of data is used to tune hyperparameters?
Answer: C
Explanation:
According to AAISM, hyperparameter tuning uses validation data, not training or test data.
Validation datasets are specifically designed to evaluate different hyperparameter configurations without contaminating the training or testing sets.
NEW QUESTION # 248
An organization plans to use AI to analyze the shopping patterns of its customers to predict interests and send targeted, customized marketing emails. Which of the following should be done FIRST?
Answer: C
Explanation:
The first action, before any processing of personal data for AI-driven profiling and targeted communications, is to establish a lawful basis for processing. Under AAISM-aligned privacy governance, explicit and informed consent is prioritized for new or sensitive uses such as interest profiling and targeted marketing. Consent ensures purpose limitation, transparency, and user control prior to model ingestion and campaign activation.
Training teams, updating terms of service, or verifying contact details are important, but they do not provide legal authority to process data; therefore, they follow after consent is obtained.
References: AI Security Management™ (AAISM) Body of Knowledge - Privacy Governance and Lawful Basis; Purpose Limitation and Transparency; Consent Management in AI-enabled Marketing. AAISM Study Guide - Data Protection Controls for AI Profiling; Consent Capture and Record-Keeping.
NEW QUESTION # 249
Which of the following MOST effectively addresses bias in generative AI models?
Answer: A
Explanation:
AAISM identifies fairness constraints as a direct mechanism to mitigate and control model bias by embedding fairness requirements into optimization objectives during training.
Data augmentation (B) helps but is not a primary anti-bias control. Adversarial training (C) focuses on robustness, not fairness. Minimization (A) reduces data, often making bias worse.
References: AAISM Study Guide - Fairness, Bias Mitigation Techniques, Ethical AI Controls.
NEW QUESTION # 250
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