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Pass Guaranteed AAISM - Reliable Valid Dumps ISACA Advanced in AI Security Management (AAISM) Exam Questions

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ISACA AAISM Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
Which of the following strategies BEST ensures generative AI tools do not expose company data?

A. Testing AI tools before implementation
B. Ensuring AI tools are compliant with local regulations
C. Conducting an independent AI data audit
D. Implementing a solution to prohibit the input of sensitive data

Answer: D

Explanation:
AAISM prioritizes preventive controls at the point of use for generative AI, specifically input-governance and DLP controls that block or redact confidential, regulated, or high-risk data before it can be sent to external models. Audits, pre-deployment tests, and regulatory conformance are necessary but do not themselves prevent an employee from pasting sensitive content into prompts. Enforcing input restrictions, pattern-based redaction, policy-aware controls, and allow-lists for approved contexts provides the highest assurance of preventing exposure.
References:* AI Security Management (AAISM) Body of Knowledge: Data loss prevention for AI; prompt
/input controls; approved channels and guardrails for generative AI.* AI Security Management Study Guide: Preventive over detective controls for confidentiality; enterprise guardrails at prompt capture and egress points.

NEW QUESTION # 27
Who is responsible for implementing recommendations in a final report after an external AI compliance audit?

A. Internal auditors
B. Model owners
C. System architects
D. End users

Answer: B

Explanation:
Under AAISM governance, management and control owners are accountable for remediation. For AI systems, the accountable role is the Model Owner (or equivalent business/service owner), who coordinates with architects, engineers, and operations to implement corrective actions and report closure. Internal auditors provide independent assurance and do not implement fixes; end users are not remediation owners.
References: AI Security Management (AAISM) Body of Knowledge - Roles & Accountability (Model Owner, Control Owner, Assurance); Audit Findings Management and Remediation Governance.

NEW QUESTION # 28
An organization is implementing AI agent development across multiple engineering teams. Which of the following is the MOST important focus of AI-specific security training for developers?

A. Dataset bias, explainability, and fairness in model decisions
B. API abuse, data leakage, and third-party plug-in risk
C. Prompt injection, agent memory control, and insecure tool execution
D. Output moderation, hallucination handling, and policy alignment

Answer: C

Explanation:
For developer-facing, near-term hardening of AI agents, AAISM prioritizes secure agent design and runtime controls against prompt injection, unsafe memory/tool use, and tool-execution compromise. These are primary exploitation paths for agents that read external content, persist memory, and call tools with elevated privileges. Training must center on: guarding tool invocation, constraining memory scope, sanitizing
/validating inputs, and isolating high-risk actions. Topics like bias/fairness (B) and policy/hallucinations (C) are important but are governance/assurance concerns; API abuse and plug-in risk (D) matter, yet the core, developer-controlled attack surface for agents is injection and unsafe tool/memory design.
References:* AI Security Management (AAISM) Body of Knowledge: Agent Security-prompt injection defenses, tool execution constraints, memory governance* AAISM Study Guide: Developer controls for agent frameworks; input validation, sandboxing, scoped permissions, guardrail patterns

NEW QUESTION # 29
Which of the following BEST describes the role of transparency in AI?

A. Explaining the AI system in an understandable and logical way so reasons for decisions can be given
B. Persuading someone that the AI tool in use is beneficial and operates as expected
C. Talking through a decision tree to better understand how the algorithm made each of its choices
D. Publishing AI mechanisms, data sources, and decision-making processes while making them openly available

Answer: A

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 # 30
Which of the following AI data management techniques involves creating validation and test data?

A. Annotating
B. Training
C. Learning
D. Splitting

Answer: D

Explanation:
Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.
References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide - Train/Validation/Test Splits and Evaluation Integrity.

NEW QUESTION # 31
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