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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 security program development and management - AI governance frameworks and alignment with business objectives - AI security policies, standards, and procedures |
| AI Technologies and Controls | 38% | - AI lifecycle security: model selection, training, validation, and deployment - Data management and protection controls for AI - Security monitoring, testing, and continuous assurance for AI systems - AI security architecture and secure design principles - Privacy, ethics, trust, and safety controls |
| AI Risk Management | 31% | - AI risk assessment, thresholds, and treatment strategies - AI threat and vulnerability management - Regulatory compliance and ethical considerations in AI - AI vendor and supply chain risk management |
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NEW QUESTION # 213
A military contractor discovered that its large language model (LLM) is at high risk of being targeted by advanced persistent threat (APT) actors seeking to exploit the model to access confidential information.
Which of the following attacks is the HIGHEST priority to protect against?
Answer: C
Explanation:
AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs-directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario's stated objective-accessing confidential information-most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.
References:* AI Security Management (AAISM) Body of Knowledge: Model Security-Inference-Time Threats (Inversion, Membership Inference) and Confidentiality Risks* AAISM Study Guide: Leakage Mitigations-Regularization, Output Minimization/Calibration, Access Controls & Monitoring on Model Interfaces
NEW QUESTION # 214
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.
NEW QUESTION # 215
An organization wants to ensure a new AI security program is in alignment with the existing information security program. Which of the following is the BEST course of action?
Answer: D
Explanation:
Defining AI-specific objectives, governance roles, and reporting responsibilities establishes a structured framework that aligns the AI security program with existing information security governance, ensuring consistency, clarity, and integration from the outset.
NEW QUESTION # 216
A programmer suspects an AI system is inferring sensitive user information. What is the BEST action?
Answer: B
Explanation:
AAISM directs that potential privacy, ethical, or compliance risks must be escalated to the AI Governance Panel, the body responsible for oversight, risk approval, and corrective action.
NEW QUESTION # 217
Which of the following should be the MOST important consideration when conducting an AI impact assessment?
Answer: A
Explanation:
AAISM defines an AI Impact Assessment (AIIA) as a structured evaluation designed to determine whether an AI system can safely and responsibly support business objectives without creating unacceptable risks.
The framework states that alignment to business objectives is the central purpose of AI adoption and therefore must be the starting point in an impact assessment.
Reputation (D) is a consideration, but it is a secondary outcome of failing to meet objectives responsibly.
Employee retention (B) and training (C) are not core drivers of an AIIA.
References: AAISM Study Guide - AI Impact Assessment; Business Alignment Principles.
NEW QUESTION # 218
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