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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Management | 31% | - Regulatory compliance and ethical considerations in AI - AI vendor and supply chain risk management - AI threat and vulnerability management - AI risk assessment, thresholds, and treatment strategies |
| Topic 2: AI Technologies and Controls | 38% | - AI lifecycle security: model selection, training, validation, and deployment - Privacy, ethics, trust, and safety controls - AI security architecture and secure design principles - Data management and protection controls for AI - Security monitoring, testing, and continuous assurance for AI systems |
| Topic 3: AI Governance and Program Management | 31% | - AI security policies, standards, and procedures - AI asset and data lifecycle management - AI security program development and management - Business continuity and incident response for AI systems - AI governance frameworks and alignment with business objectives |
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NEW QUESTION # 79
A regulator warns of increased risk of AI re-identification attacks on anonymized datasets. What should the information security manager do FIRST?
Answer: A
Explanation:
AAISM states that anonymization is not permanent and may be reversible through re-identification attacks.
The first action should be to evaluate and measure the actual privacy risk through:
* adversarial re-identification testing
* privacy audits
* monitoring for misuse
This provides the factual basis needed before making destructive or operational decisions.
Access control (D) is important but not the FIRST step. Deleting datasets (B) is premature. Assuming anonymization is permanent (A) violates AI privacy principles.
References: AAISM Study Guide - Privacy Risk, Re-Identification Threats, Adversarial Testing.
NEW QUESTION # 80
Which of the following is MOST important when evaluating the quality of data to be ingested into an AI model?
Answer: D
Explanation:
Data profiling and cleansing ensure the dataset is accurate, consistent, and free of errors or anomalies, which directly determines the quality and reliability of the model's outputs.
NEW QUESTION # 81
An organization is designing an AI-based credit risk assessment system integrating sensitive financial data.
Which option BEST supports security-by-design?
Answer: A
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
Differential privacy (A) is powerful but is a mitigation, not the overarching design control. Segmentation (C) and IP allow lists (D) are supporting controls but not the foundational step in secure design.
References: AAISM Study Guide - Security-by-Design; AI Threat Modeling.
NEW QUESTION # 82
Which of the following attacks has the PRIMARY objective of compromising the confidentiality and privacy of information used in an AI system?
Answer: C
Explanation:
Model inversion aims to reconstruct or infer sensitive information from the model's training data, directly compromising confidentiality and privacy.
NEW QUESTION # 83
A healthcare provider uses a third-party large language model (LLM) to summarize patient records. A risk analysis indicates a high likelihood of potential data leakage in excess of the organization's risk tolerance. Which of the following is the BEST course of action taken by the vendor to reduce risk to an acceptable level?
Answer: B
Explanation:
Migrating the LLM workload to a private instance and restricting access most effectively reduces the risk of sensitive patient data leakage. This approach provides stronger control over data handling, processing environments, access management, and exposure pathways, making it the most appropriate risk reduction measure for healthcare data.
NEW QUESTION # 84
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