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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Real Exam Qty: | 90 |
| Available Languages: | Spanish, English |
| Passing Score: | 450/800 |
| Exam Duration: | 150 minutes |
| Exam Price: | USD 459 (member) / USD 599 (non-member) |
| Certificate Validity Period: | 3 years (continuing professional education required for renewal) |
| Related Certifications: | CISSP CISM |
| Exam Format: | Multiple-choice questions |
| Recommended Training: | ISACA Official Training & Review Materials |
| Exam Registration: | ISACA AAISM Credentialing Page |
| Sample Questions: | ISACA AAISM Sample Questions |
| Exam Way: | Computer-based exam (online proctored or authorized test center) |
| Pre Condition: | Recommended: Active CISM or CISSP certification and experience in IT/security management or advisory roles |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaism |
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NEW QUESTION # 395
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: D
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 # 396
Which of the following BEST describes an adversarial attack on an AI model?
Answer: D
Explanation:
AAISM defines adversarial attacks as manipulations of input data (text, image, audio, numeric values) designed to cause the model to produce incorrect or harmful predictions.
Hardware attacks (A) are infrastructure threats. Social engineering (C) targets people, not models. DoS attacks (D) affect availability, not model decision pathways.
References: AAISM Study Guide - Adversarial Threats; Input Manipulation.
NEW QUESTION # 397
Which of the following is the MOST likely cause of model drift?
Answer: D
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 # 398
Which of the following is MOST important for an organization to consider when implementing a preventive security safeguard into a new AI product?
Answer: B
Explanation:
AAISM materials emphasize that the most effective preventive safeguard is to ensure input sanitization. Preventive controls stop malicious or malformed inputs from reaching the model in the first place, thereby reducing the likelihood of prompt injection, evasion, or poisoning at inference time. Model output monitoring is a detective control, not preventive. Penetration testing is an assessment technique rather than a safeguard. Differential privacy protects data privacy but does not prevent adversarial input manipulation. Therefore, the most important preventive safeguard in a new AI product is robust input sanitization.
NEW QUESTION # 399
A financial organization relies on AI-based identity verification and fraud detection services.
Which of the following BEST integrates AI security risk into the business continuity plan (BCP)?
Answer: D
Explanation:
AAISM stresses that AI systems and their supporting infrastructure must be explicitly included in disaster recovery and continuity planning, since disruptions to models, feature stores, or pipelines can halt critical business functions.
NEW QUESTION # 400
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