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In this way, you can clear all your doubts and understand each topic well. ISACA Dumps PDF are customizable and simulate the real ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) test scenario. The desktop-based AAISM Practice Exam software works on Windows. The web-based AAISM practice exam is compatible with all operating systems and browsers.

ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 2
  • AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 3
  • AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

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Latest ISACA AAISM Guide Files | AAISM New Dumps Questions

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
Which of the following MOST effectively addresses bias in generative AI models?

Answer: C

Explanation:
AAISM identifies fairness constraints as a direct mechanism to mitigate and control model bias by embedding fairness requirements into optimization objectives during training.
Data augmentation (B) helps but is not a primary anti-bias control. Adversarial training (C) focuses on robustness, not fairness. Minimization (A) reduces data, often making bias worse.
References: AAISM Study Guide - Fairness, Bias Mitigation Techniques, Ethical AI Controls.


NEW QUESTION # 24
An organization uses an AI tool to scan social media for product reviews. Fraudulent social media accounts begin posting negative reviews attacking the organization's product. Which type of AI attack is MOST likely to have occurred?

Answer: D

Explanation:
The AAISM materials classify availability attacks as attempts to disrupt or degrade the functioning of an AI system so that its outputs become unreliable or unusable. In this scenario, the fraudulent social media accounts are deliberately overwhelming the AI tool with misleading negative reviews, undermining its ability to deliver accurate sentiment analysis. This aligns directly with the concept of an availability attack. Model inversion relates to reconstructing training data from outputs, deepfakes involve synthetic content generation, and data poisoning corrupts the training set rather than manipulating inputs at runtime. Therefore, the fraudulent review campaign is most accurately identified as an availability attack.
References:
AAISM Study Guide - AI Risk Management (Adversarial Threats and Availability Risks) ISACA AI Security Management - Attack Classifications


NEW QUESTION # 25
Security and assurance requirements for AI systems should FIRST be embedded in the:

Answer: B

Explanation:
AAISM directs organizations to embed security, safety, and compliance controls at design time ("secure- by-design" and "shift-left"), ensuring requirements for robustness, privacy, and governance are defined as non-functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.
References:* AI Security Management (AAISM) Body of Knowledge: Governance-Secure-by-Design; Policy-to-Control Traceability; Requirements Management* AAISM Study Guide: AI Program Lifecycle- Planning & Design Controls; Design-time Threat Modeling and Control Selection* AAISM Mapping to Standards: Design-phase Risk Identification and Requirements Engineering for AI


NEW QUESTION # 26
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: A

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.
Explainability (A) and retraining (B) are operational improvements, not continuity mechanisms. Multi-zone redundancy (D) improves availability but does not represent complete BCP integration.
References: AAISM Study Guide - AI Business Continuity and Resilience Requirements.


NEW QUESTION # 27
Which of the following BEST strengthens information security controls around the use of generative AI applications?

Answer: D

Explanation:
AAISM identifies continuous monitoring of AI outputs-especially generative outputs-as the most effective security control, ensuring that violations, unsafe responses, data leakage, and policy-breaking behavior are detected and corrected.
A kill switch (C) is a last-resort measure, not a primary control. Exceeding benchmarks (A) does not ensure relevance. Validating training data (D) is important but insufficient for generative output risks.
References: AAISM Study Guide - Generative AI Security Controls; Output Monitoring and Policy Alignment.


NEW QUESTION # 28
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