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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Certificate Validity Period: | 3 years |
| Exam Price: | USD $399 |
| Exam Duration: | 150 minutes |
| Related Certifications: | ISACA Advanced in AI Security Management (AAISM) |
| Passing Score: | 450/800 |
| Exam Format: | Multiple Choice, Scenario-based Questions |
| Available Languages: | Spanish, English |
| Real Exam Qty: | 90 |
| Sample Questions: | ISACA AAISM Sample Questions |
| Exam Way: | Online remote proctored exam or test center exam |
| Pre Condition: | Candidates must 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 # 154
An organization decides to use an anomaly-based intrusion detection system (IDS) integrated with a generative adversarial network-enabled AI tool. The integrated tool would MOST effectively detect intrusions by leveraging:
Answer: D
Explanation:
AAISM describes GANs as effective for synthetic data generation to augment scarce or imbalanced security datasets. In anomaly IDS contexts, GANs can create realistic synthetic attack traffic and edge-case behaviors that improve detector sensitivity and robustness. While labeled "real" data is valuable, the specific advantage of a GAN-integrated pipeline is the capability to generate adversarially realistic synthetic intrusions for training and stress testing. Automated rules are a signature-based paradigm and do not leverage GAN strengths; validation sets are for evaluation, not primary improvement of anomaly coverage.
References:* AI Security Management (AAISM) Body of Knowledge: Security data engineering; synthetic data via generative models for rare-event detection; adversarial augmentation for IDS.* AI Security Management Study Guide: Model robustness with synthetic adversarial examples; training-set enrichment for anomaly detection.
NEW QUESTION # 155
Which of the following BEST describes the role of transparency in AI?
Answer: D
Explanation:
Transparency in AI is a governance principle requiring that systems be explainable to stakeholders in ways that are understandable and meaningful, enabling clear articulation of how decisions were reached and why.
Within an AI program, transparency supports accountability, auditability, and trust by ensuring that reasons for decisions can be communicated and scrutinized. Option C reflects this definition by focusing on intelligible, logical explanations of system behavior and decision rationale.
Option A is a narrow technique (model-specific interpretability for decision trees) and does not capture transparency as a broad governance requirement. Option B conflates transparency with full public disclosure; transparency does not require making all artifacts openly available. Option D is persuasion/advocacy, not transparency.
References: AI Security Management (AAISM) Body of Knowledge: "AI Governance-Transparency and Explainability," "Accountability and Assurance"; AAISM Study Guide: "Explainability Objectives and Stakeholder Communication," "Documentation for Decision Rationale."
NEW QUESTION # 156
A financial services firm received a regulatory fine after a vendor switched its chatbot's AI model without due diligence, resulting in unethical investment advice to the firm's clients. Which of the following controls should be implemented by the firm to BEST prevent recurrence of this scenario?
Answer: D
Explanation:
AAISM requires formal change management for AI systems, including vendor-initiated changes:
pre-approval, documented impact assessment (ethics/compliance/performance), regression testing, sign-off by accountable owners, and traceable release records.
NEW QUESTION # 157
Which of the following information is MOST important to include in a centralized AI inventory?
Answer: B
Explanation:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.
References:
AAISM Exam Content Outline - AI Governance and Program Management (AI Inventories and Oversight) AI Security Management Study Guide - Ownership and Accountability Structures
NEW QUESTION # 158
During training, the PRIMARY security benefit of using synthetic data derived from real records is:
Answer: C
Explanation:
Synthetic data derived from real records helps reduce the risk that individuals can be directly identified from training datasets. This lowers re-identification risk while still enabling AI models to learn useful patterns from representative data.
NEW QUESTION # 159
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