Exam AAISM Questions Pdf & AAISM Reliable Test Objectives

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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q359-Q364):

NEW QUESTION # 359
Which of the following is the BEST way to reduce the risk of misuse of an AI agent that has access to critical data and systems?

Answer: D

Explanation:
AAISM frameworks highlight output-based controls, including output filtering, restriction validation, and policy-aligned guardrails as primary defenses for AI agents with high privileges. Ensuring the agent does not output unauthorized instructions or sensitive data directly mitigates misuse.
Allowing user configuration (B) increases risk. Prohibiting manipulation entirely (C) is impractical. Reducing human oversight (D) increases system abuse potential.
References: AAISM Study Guide - AI Agents, Output Controls, and Guardrails.


NEW QUESTION # 360
Which of the following features of training data can be used to provide data provenance?

Answer: A

Explanation:
Data tags provide metadata that identifies the origin, ownership, handling requirements, and history of training data. This supports data provenance by enabling traceability and verification of where the data came from and how it has been managed throughout its lifecycle.


NEW QUESTION # 361
An AI system that supports critical processes has deviated from expected performance and is producing biased outcomes. Which of the following is the BEST course of action?

Answer: A

Explanation:
AAISM directs that when harmful or biased behavior is observed in a production AI system, the organization should enter a formal incident/variance handling workflow that begins with root cause analysis (RCA) to identify the source of deviation (data drift, concept drift, feature leakage, pipeline changes, control failures) and determine proportionate risk treatments. Immediate retraining (Option A) without RCA risks reinforcing the same bias; audits (Option C) are key activities within RCA rather than the action that frames the response; a kill switch (Option D) is reserved for conditions where risk exceeds the defined tolerances and immediate harm prevention is required.
References: AI Security Management (AAISM) Body of Knowledge - Incident Response & Post-Incident Improvement; Model Risk Treatment & Drift Management; Bias Detection and Remediation Governance.


NEW QUESTION # 362
Which of the following BEST enables an organization to maintain visibility to its AI usage?

Answer: C

Explanation:
The AAISM framework stresses that the most effective way to maintain oversight of organizational AI usage is by maintaining a comprehensive inventory of all AI systems and the business units using them. Such an inventory provides a centralized, transparent record of where AI is deployed, ensuring accountability, monitoring, and compliance. While board approval, dashboards, and KPIs are important governance tools, they do not provide holistic visibility across the enterprise. The inventory ensures traceability and governance alignment, making it the best method to maintain visibility of AI usage.


NEW QUESTION # 363
An organization implementing an LLM application sees unexpected cost increases due to excessive computational resource usage. Which vulnerability is MOST likely in need of mitigation?

Answer: A

Explanation:
AAISM categorizes unbounded consumption (also known as "resource exhaustion" or "infinite queries") as an AI-specific vulnerability where attackers (or faulty prompts) trigger excessive computation, leading to high costs and degraded service.


NEW QUESTION # 364
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