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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Exam Duration: | 150 minutes |
| Exam Format: | Multiple-choice, Scenario-based |
| Related Certifications: | CISSP CISM |
| Passing Score: | 450 (out of 800) |
| Certificate Validity Period: | 3 years |
| Available Languages: | English |
| Real Exam Qty: | 90 |
| Exam Price: | US$459 (members), US$599 (non-members) |
| Recommended Training: | ISACA Official AAISM Training |
| Exam Registration: | ISACA AAISM Registration |
| Sample Questions: | ISACA AAISM Sample Questions |
| Exam Way: | Computer-based; available at authorized PSI testing centers or via remote proctoring |
| Pre Condition: | Hold an active CISM or CISSP certification |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline |
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NEW QUESTION # 325
A financial organization is concerned about the risk of prompt injection attacks on its customer service chatbot. Which of the following controls BEST addresses this concern?
Answer: A
Explanation:
AAISM emphasizes preventive technical controls for LLM threats such as prompt injection, including input validation/sanitization, instruction isolation, allow/deny lists, context segmentation, and output filtering.
These reduce the model's exposure to adversarial instructions embedded in user prompts or retrieved context.
Monitoring (A) is detective, not preventive; increasing parameters (B) does not inherently improve security against injection; human-in-the-loop (D) is valuable for high-risk decisions but does not directly neutralize injection vectors at the control boundary the way input validation and content filtering do.
References: AI Security Management (AAISM) Body of Knowledge - Technical Controls for LLM Security; Input/Output Filtering and Context Isolation; Secure Inference and Prompt Injection Mitigations.
NEW QUESTION # 326
Which of the following control strategies BEST mitigates the risk of user harm from a large language model (LLM) generating offensive or biased content?
Answer: C
Explanation:
Output filtering and moderation directly address the risk of harmful, offensive, or biased content by reviewing and controlling generated responses before they reach users. This helps enforce safety, ethical, and compliance standards in LLM outputs.
NEW QUESTION # 327
The PRIMARY AI governance risk of inconsistently applying data augmentation and cleansing across datasets is that the model may:
Answer: A
Explanation:
Inconsistent data augmentation and cleansing can introduce or amplify bias within datasets, resulting in unreliable or unfair model behavior. This undermines both data integrity and model integrity, creating significant governance, ethical, and operational risks.
NEW QUESTION # 328
The PRIMARY purpose of adopting and implementing AI architecture as part of an organizational AI program is to:
Answer: D
Explanation:
An AI architecture, within program governance, exists to align AI system components and lifecycle processes with business goals and policy constraints. Architecture provides the organizing structure linking strategy, capabilities, processes, data, models, controls, and assurance so that AI outcomes are traceable to business value, risk appetite, and compliance expectations. Efficiency, speed, and threat analysis are important architectural qualities, but they are not the primary purpose; the primary purpose is strategic and governance alignment so that technical choices and controls consistently realize organizational objectives.
NEW QUESTION # 329
Which of the following approaches BEST enables the separation of sensitive and shareable data to prevent an AI chatbot from inadvertently disclosing confidential information?
Answer: B
Explanation:
AAISM materials describe data segregation and segmented access as core technical controls to prevent unintended information disclosure by AI systems. Siloing refers to logically or physically separating data into distinct repositories or contexts, ensuring that sensitive datasets are not available to components or applications that only require non-sensitive information. This is directly aligned with preventing a chatbot from accessing or mixing confidential data with general conversational content. Zero Trust (A) is an overarching security architecture principle, focusing on identity and continuous verification; it does not by itself guarantee separation of data. Sandboxing (B) isolates processes but is less about fine-grained data separation. Containerization (D) packages applications and their dependencies, again not necessarily solving the specific problem of mixing sensitive and non-sensitive datasets. Siloing is explicitly highlighted as a way to prevent cross-context leakage in AI use cases.
References: AI Security Management™ (AAISM) Study Guide - Technical Controls for AI Data Protection; Data Segregation and Access Boundaries.
NEW QUESTION # 330
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