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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Management | 31% | - AI Threats and Vulnerabilities
|
| AI Governance and Program Management | 31% | - Frameworks and Compliance
|
| AI Technologies and Controls | 38% | - AI Assurance and Safety
|
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NEW QUESTION # 203
A large language model (LLM) has been manipulated to provide advice that serves an attacker's objectives.
Which of the following attack types does this situation represent?
Answer: B
Explanation:
AAISM categorizes the manipulation of an LLM at inference time, where crafted inputs cause outputs to serve attacker objectives, as an evasion attack. Evasion attacks exploit weaknesses in the model's decision- making boundaries by altering queries to produce compromised or misleading outputs. Privilege escalation refers to unauthorized access rights, data poisoning targets the training phase, and model inversion reconstructs training data. In this case, manipulation of outputs to align with an attacker's goals reflects an evasion attack.
References:
AAISM Exam Content Outline - AI Risk Management (Adversarial Attack Types) AI Security Management Study Guide - Evasion and Manipulation Risks
NEW QUESTION # 204
Which of the following is the MOST important consideration when deciding how to compose an AI red team?
Answer: D
Explanation:
AAISM materials specify that the composition of an AI red team must be tailored to the organization's AI use cases. The purpose of red-teaming is to simulate realistic adversarial conditions aligned with the actual applications of AI. For example, testing a generative model requires different expertise than testing a fraud detection system. While resource availability, compliance requirements, and time-to-market pressures are practical considerations, they are secondary to aligning team expertise with use case scenarios. The most important factor is therefore the AI use cases themselves.
References:
AAISM Exam Content Outline - AI Risk Management (Red Teaming Considerations) AI Security Management Study Guide - Tailoring Adversarial Testing to Use Cases
NEW QUESTION # 205
A financial organization uses AI to detect potential fraudulent activities but is concerned about the impact of potential data poisoning. Which of the following controls would BEST mitigate this risk?
Answer: D
Explanation:
AAISM identifies training-data diversity and provenance assurance as primary treatments against data poisoning. Sourcing data from multiple, independently governed providers, combined with ingestion validation and anomaly screening, reduces the chance that a single compromised source can skew model behavior and improves cross-source consistency checks. Transparency, break-glass, and awareness are valuable but do not directly reduce poisoning exposure at the training boundary.
NEW QUESTION # 206
Which of the following BEST helps an organization protect confidential data while using a pre- trained open-source AI solution?
Answer: A
Explanation:
Verifying and sanitizing inputs prevents sensitive data from being unintentionally exposed to the model, while sanitizing outputs helps ensure that confidential information is not leaked in responses, directly protecting data during use.
NEW QUESTION # 207
The PRIMARY purpose of adopting and implementing AI architecture within an organizational AI program is to:
Answer: C
Explanation:
AAISM describes AI architecture as a strategic alignment framework, ensuring AI systems, data pipelines, governance processes, and capabilities match organizational business goals and regulatory requirements.
NEW QUESTION # 208
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