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| Section | Weight | Objectives |
|---|---|---|
| AI Technologies and Controls | 38% | - Security monitoring, testing, and continuous assurance for AI systems - Data management and protection controls for AI - AI lifecycle security: model selection, training, validation, and deployment - Privacy, ethics, trust, and safety controls - AI security architecture and secure design principles |
| AI Governance and Program Management | 31% | - AI security program development and management - AI asset and data lifecycle management - AI governance frameworks and alignment with business objectives - AI security policies, standards, and procedures - Business continuity and incident response for AI systems |
| 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 # 361
A red team tests an AI chatbot by simulating adversarial suffix attacks and multilingual inputs.
Which of the following is the PRIMARY goal of this exercise?
Answer: A
Explanation:
Adversarial suffix attacks and multilingual input manipulation are commonly used to test whether an AI chatbot can be tricked into bypassing safeguards or executing unintended behavior. The primary goal is to evaluate the chatbot's resilience against prompt injection and adversarial manipulation techniques.
NEW QUESTION # 362
An organization's HR chatbot was discovered generating fake employee details using a third- party API. Which would be the information security team's BEST recommendation?
Answer: D
Explanation:
The issue stems from the chatbot relying on an external API that is producing unreliable outputs.
Limiting or restricting communication with the third-party API reduces exposure to untrusted responses and prevents further generation of incorrect or fabricated employee information, directly mitigating the risk.
NEW QUESTION # 363
Which of the following is the MOST likely cause of model drift?
Answer: C
Explanation:
Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data- centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high-likelihood driver of observable drift in production because it changes the effective data-generating process the model learns from. In contrast:
* Perfect knowledge is an attacker capability descriptor, not a drift cause.
* Membership inference targets privacy of the training set and does not inherently shift data distributions.
* Model stealing targets IP/confidentiality; it does not change the victim model's data distribution or decision boundary in situ.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Risk & Drift; Data Integrity Risks; Adversarial ML-Poisoning vs. Evasion* AAISM Study Guide: Production Monitoring & Drift Management; Risk Scenarios-Data Poisoning Impacts and Controls* AAISM Mapping to Standards:
Lifecycle Risk Treatment-Robustness to Data Contamination; Continuous Monitoring and Feedback
NEW QUESTION # 364
Which of the following mitigation control strategies would BEST reduce the risk of introducing hidden backdoors during model fine-tuning via third-party components?
Answer: B
Explanation:
AAISM highlights threat modeling and supply chain integrity checks as key controls for managing AI-specific risks, including hidden backdoors in third-party models, libraries, or fine-tuning artifacts. The official guidance states that organizations should "identify adversarial insertion points, verify component integrity, and continuously test for malicious behaviors introduced via external components." This is precisely what option B describes.
NEW QUESTION # 365
To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:
Answer: A
Explanation:
AAISM highlights that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.
NEW QUESTION # 366
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