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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Available Languages: | English |
| Related Certifications: | CISSP CISM |
| Exam Format: | Multiple-choice, Scenario-based |
| Exam Price: | US$459 (members), US$599 (non-members) |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 150 minutes |
| Passing Score: | 450 (out of 800) |
| Real Exam Qty: | 90 |
| 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 |
Test your knowledge of the AAISM exam dumps with ISACA AAISM practice questions. The software is designed to help with ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) exam dumps preparation. ISACA AAISM Practice Test software can be used on devices that range from mobile devices to desktop computers.
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NEW QUESTION # 407
Which of the following approaches BEST helps to reduce model bias?
Answer: C
Explanation:
AAISM frames bias risk primarily as a data problem. The most impactful mitigation is to ensure diversity and representativeness of training data sources, thereby reducing sampling bias and improving fairness across subpopulations. More labels per instance (A) does not correct coverage gaps; reducing update cadence (B) can entrench existing bias; and higher model complexity (C) may overfit or obscure bias without addressing root causes. Diverse, representative datasets-paired with fairness testing-are the recommended first-line control.
References: AI Security Management™ (AAISM) Body of Knowledge - Bias Identification and Mitigation; Data Quality and Representativeness. AAISM Study Guide - Fairness Risk Controls; Dataset Curation and Coverage Analysis.
NEW QUESTION # 408
When creating a use case for an AI model that provides sensitive decisions affecting end users, which of the following is the GREATEST benefit of using model cards?
Answer: C
Explanation:
AAISM highlights that model cards are a governance tool designed to document ethical considerations, limitations, fairness constraints, data sources, and suitability of use cases for AI models--especially when they affect individuals' rights, opportunities, or access to services.
Their greatest value is providing transparency and ethical clarity, ensuring stakeholders understand risks, bias considerations, and how decisions impact users.
NEW QUESTION # 409
What is the GREATEST benefit of performing AI security risk assessments?
Answer: A
Explanation:
AAISM emphasizes that the primary value of AI security risk assessments is prioritizing risks based on likelihood, impact, and business relevance.
NEW QUESTION # 410
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?
Answer: C
Explanation:
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing. While differential privacy (C) is a strong control for leakage risk, it is one control choice among many and should be selected as a result of threat modeling. IP allow lists (B) and container segmentation (A) are valuable hardening measures but are narrower and do not replace the lifecycle-wide governance and design traceability that threat modeling enables.
References: AI Security Management™ (AAISM) Body of Knowledge - Secure AI SDLC; AI Threat Modeling and Abuse Case Development; Architecture & Control Selection; Risk-Based Design Assurance.
AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.
NEW QUESTION # 411
An organization is adopting an agentic AI solution from an external vendor to support its internal IT operations. To evaluate the security posture of this system, which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?
Answer: A
Explanation:
Third-party audit reports provide independent assurance that the vendor's stated controls are designed and operating effectively against recognized criteria. Such attestations (e.g., audit/assurance frameworks) are traceable, repeatable, and verifiable, and they support supply-chain risk reviews and contractual assurance.
Internal red-team reports are not independent, industry "peer reviews" are not control attestations, and whitepapers are marketing/educational materials without evidence of control operation.
References: AI Security Management (AAISM) Body of Knowledge: Third-Party & Supply-Chain Assurance; Independent Assessment and Audit Evidence; Vendor Risk Management. AAISM Study Guide:
Assurance Artifacts for AI Services; Evaluating External AI Providers.
NEW QUESTION # 412
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