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| Section | Weight | Objectives |
|---|---|---|
| AI Governance and Program Management | 31% | - Business continuity and incident response for AI systems - AI asset and data lifecycle management - AI governance frameworks and alignment with business objectives - AI security policies, standards, and procedures - AI security program development and management |
| AI Technologies and Controls | 38% | - Data management and protection controls for AI - AI security architecture and secure design principles - Security monitoring, testing, and continuous assurance for AI systems - Privacy, ethics, trust, and safety controls - AI lifecycle security: model selection, training, validation, and deployment |
| 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 |
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NEW QUESTION # 284
A vendor switched its chatbot's AI model without due diligence, causing unethical investment advice. What control BEST prevents this scenario?
Answer: C
Explanation:
AAISM stresses AI-specific change management as essential for vendor-driven or system-driven updates. Proper change control includes:
- impact assessments
- ethical review
- risk evaluation
- approval checkpoints
- rollback plans
NEW QUESTION # 285
For a life insurance company deploying AI for fraud detection, which factor is MOST critical?
Answer: A
Explanation:
AAISM emphasizes robustness as the key requirement for fraud-detection systems because they must resist adversarial manipulation, data poisoning, spoofing, and input tampering.
Accuracy (B) matters but does not protect against adversarial attacks. Explainability (C) is important but secondary. Adaptability (D) is useful but not the top security requirement.
References: AAISM Study Guide - AI Robustness; Fraud Detection and Adversarial Resistance.
NEW QUESTION # 286
As organizations increasingly rely on vendors to develop AI systems, which of the following is the MOST effective way to monitor vendors and ensure compliance with ethical and security standards?
Answer: C
Explanation:
AAISM vendor governance guidance identifies regular audits of vendor processes as the most effective method of ensuring compliance with ethical and security standards. Independent audits provide verifiable assurance that vendors are meeting agreed-upon requirements. Self- attestation, internal monitoring, or documentation sharing provide some transparency but do not guarantee compliance. The best practice, particularly for high-risk AI deployments, is independent and recurring audits of vendor processes.
NEW QUESTION # 287
Which of the following is the MOST serious consequence of an AI system correctly guessing the personal information of individuals and drawing conclusions based on that information?
Answer: D
Explanation:
The AAISM curriculum states that the most serious privacy concern occurs when AI systems infer and disclose sensitive personal or group information without the knowledge or consent of the individuals. This constitutes a direct breach of privacy rights and data protection principles, including those enshrined in GDPR and other global regulations. While litigation, reputational damage, or loss of trust are significant consequences, the unauthorized revelation of personal information through inference is classified as the most severe, because it directly undermines individual autonomy and confidentiality.
NEW QUESTION # 288
Which of the following strategies is the MOST effective way to protect against AI data poisoning?
Answer: D
Explanation:
AAISM identifies robust data validation and anomaly detection on incoming training data as the primary defense against data poisoning. These controls detect corrupted, manipulated, or adversarial samples before they enter the training pipeline.
Diverse data (A) is helpful but not protective against poisoning. More complexity (B) does not mitigate poisoning and can worsen vulnerability. More features (D) increases attack surface.
References: AAISM Study Guide - AI Threats; Data Poisoning Mitigation through Validation & Anomaly Detection.
NEW QUESTION # 289
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