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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Exam Format: | Scenario-based, Multiple-choice |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 150 minutes |
| Available Languages: | English |
| Passing Score: | 450 (out of 800) |
| Exam Price: | US$459 (members), US$599 (non-members) |
| Real Exam Qty: | 90 |
| Related Certifications: | CISM CISSP |
| 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 # 163
Which of the following is BEST for analyzing true positives, true negatives, false positives, and false negatives produced by an AI model?
Answer: C
Explanation:
A confusion matrix explicitly tabulates TP, TN, FP, FN, serving as the basis for derived metrics (precision, recall, F1, specificity) and error analysis. Precision and recall are single metrics derived from the matrix; hyperparameter tuning is a process, not an analysis artifact.
References: AAISM Body of Knowledge: Model Evaluation & Assurance-Classification Metrics and Error Analysis; AAISM Study Guide: Confusion Matrix Fundamentals and Derived Measures.
NEW QUESTION # 164
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?
Answer: C
Explanation:
Evasion attacks manipulate inputs to induce misclassification while leaving the model unchanged. AAISM prescribes adversarial robustness controls, with adversarial training as a primary measure: incorporate adversarially perturbed examples into training/validation to harden decision boundaries and improve resilience across threat models (e.g., Lp-bounded perturbations). Monitoring (A) is detective, not preventive.
Restricting parameter access (C) protects confidentiality but does not mitigate input-space attacks.
Differential privacy (D) addresses training data leakage, not robustness to adversarial inputs.
References:AI Security Management™ (AAISM) Body of Knowledge: Adversarial ML-Evasion vs.
Poisoning; Robustness and Resilience Controls; Adversarial Training.AAISM Study Guide: Model Hardening Techniques; Evaluation of Robust Accuracy; Security Testing with Adversarial Examples.
NEW QUESTION # 165
Which approach should an organization prioritize to effectively verify the security of its AI models?
Answer: B
Explanation:
The AAISM standard explicitly states that traditional penetration tests alone are insufficient for AI systems. Effective AI security testing requires:
- AI-specific threat modeling (e.g., data poisoning, prompt injection, model theft)
- Adversarial attack simulations (white-box, black-box, gradient-based attacks)
- Evaluation of robustness and manipulation resistance
NEW QUESTION # 166
Which of the following is the BEST mitigation control for membership inference attacks on AI systems?
Answer: A
Explanation:
Membership inference attacks attempt to determine whether a particular data point was part of a model's training set, which risks violating privacy. The AAISM study guide highlights differential privacy as the most effective mitigation because it introduces mathematical noise that obscures individual contributions without significantly degrading model performance. Ensemble methods improve robustness but do not specifically protect privacy. Threat modeling and red teaming help identify risks but are not direct controls. The explicit mitigation control aligned with privacy preservation for membership inference is differential privacy.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Techniques) ISACA AI Security Management - Membership Inference Mitigations
NEW QUESTION # 167
Which of the following controls BEST mitigates the inherent limitations of generative AI models?
Answer: A
Explanation:
The AAISM governance framework emphasizes that the inherent limitations of generative AI-- including hallucinations, bias, and unpredictability--are best mitigated by human oversight.
Human-in-the-loop review ensures that outputs are validated before being used in sensitive or high-risk contexts. Regulatory adoption, system classification, and reverse engineering all play supporting roles but do not directly safeguard against the model's inherent unpredictability.
Governance best practices highlight human oversight as the critical safeguard.
NEW QUESTION # 168
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