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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Management | 31% | - Regulatory compliance and ethical considerations in AI - AI risk assessment, thresholds, and treatment strategies - AI threat and vulnerability management - AI vendor and supply chain risk management |
| Topic 2: AI Governance and Program Management | 31% | - Business continuity and incident response for AI systems - AI governance frameworks and alignment with business objectives - AI asset and data lifecycle management - AI security policies, standards, and procedures - AI security program development and management |
| Topic 3: 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 - Security monitoring, testing, and continuous assurance for AI systems - Privacy, ethics, trust, and safety controls |
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NEW QUESTION # 93
A financial services firm received a regulatory fine after a vendor switched its chatbot's AI model without due diligence, resulting in unethical investment advice to the firm's clients. Which of the following controls should be implemented by the firm to BEST prevent recurrence of this scenario?
Answer: D
Explanation:
AAISM requires formal change management for AI systems, including vendor-initiated changes:
pre-approval, documented impact assessment (ethics/compliance/performance), regression testing, sign-off by accountable owners, and traceable release records.
NEW QUESTION # 94
An organization recently introduced a generative AI chatbot that can interact with users and answer their queries. Which of the following would BEST mitigate hallucination risk identified by the risk team?
Answer: C
Explanation:
AAISM highlights fine-tuning foundational models as one of the most effective strategies for reducing hallucination risk. By tailoring the model with domain-specific, curated, and verified datasets, organizations can reduce the frequency of irrelevant or fabricated outputs. Testing and validation help evaluate risks but do not directly minimize hallucinations. Training on larger datasets may improve generalization but does not guarantee accuracy. Developer training in AI risk supports governance but is not a technical control against hallucinations. The best mitigation is fine-tuning to align the chatbot with trusted, context-specific knowledge.
NEW QUESTION # 95
An AI application development team has been given access to user information and now must format it to be readable by the AI model. During which phase of the data life cycle would this MOST likely occur?
Answer: D
Explanation:
According to AAISM's data life-cycle model, data preparation is the phase where raw data is transformed into a model-ready format. The materials describe this phase as including "cleaning, encoding, formatting, feature engineering, and other transformations required for model consumption." This directly matches the scenario where a team formats user information to be readable by an AI model. Data minimization (A) is about reducing data to the minimum necessary for the stated purpose. Data collection (C) focuses on acquiring data from different sources. Data normalization (D) is a specific technique (often a sub-activity within preparation) that adjusts numeric values to a common scale; it is narrower than the broader concept of preparation.
Therefore, the activity described is correctly associated with data preparation, which the AAISM framework clearly positions before training and evaluation.
References: AI Security Management™ (AAISM) Study Guide - AI Data Life Cycle; Data Preparation and Pre-processing.
NEW QUESTION # 96
A security operation center (SOC) analyst detects repetitive API calls sending inputs to an AI solution. Which of the following attacks has MOST likely occurred?
Answer: B
Explanation:
Repetitive API calls with systematically crafted inputs are characteristic of attempts to query a model extensively to reconstruct its behavior or parameters, which is indicative of a model extraction attack.
NEW QUESTION # 97
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 # 98
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