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ISACA AAISM Exam Syllabus Topics:

Topic	Details
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 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 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 (Q284-Q289):

NEW QUESTION # 284
Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models leveraging fraud detection systems?

A. Increasing model training speed for an efficient launch
B. Reducing computational resources required for the model training phase
C. Enhancing the accuracy of predictions to desired levels
D. Protecting individual data contributions while allowing statistical analysis

Answer: D

Explanation:
Differential privacy aims to protect the privacy of any single individual's data contribution while still enabling useful aggregate learning and statistical analysis. Noise mechanisms are calibrated so that results remain informative for modeling (e.g., fraud patterns) without revealing whether any particular person's data was included or enabling inference about them. Accuracy, speed, and compute efficiency can be secondary considerations, but the primary objective is privacy protection with utility preserved.

NEW QUESTION # 285
A hospital is conducting an AI impact assessment for a proposed diagnostic imaging tool. To ensure conformity with regulatory and ethical standards, which risk requires the MOST domain- specific assessment beyond what is typically found in a standard IT risk assessment?

A. Noncompliance with data residency laws may occur if patient data is processed in a cloud region outside the required jurisdiction.
B. System downtime may increase due to hardware failure in the hosting environment, preventing medical professionals from accessing the tool.
C. Software vulnerabilities in the application's web server may allow an attacker to gain unauthorized access to the system.
D. The model may produce systematically biased diagnoses for specific demographics, potentially leading to widespread clinical harm.

Answer: D

Explanation:
Bias in diagnostic outputs directly affects clinical decision-making and patient outcomes, requiring specialized medical, ethical, and regulatory expertise beyond standard IT risk assessments. This type of risk involves domain-specific evaluation of fairness, safety, and potential harm to patients.

NEW QUESTION # 286
Which of the following strategies BEST ensures generative AI tools do not expose company data?

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

Answer: C

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 # 287
Which of the following AI system vulnerabilities is MOST easily exploited by adversaries?

A. Inaccurate generalizations from new data by the AI model
B. Inability to detect input modifications causing inappropriate AI outputs
C. Lack of protection against denial of service (DoS) attacks
D. Weak controls for access to the AI model

Answer: D

Explanation:
AAISM study materials stress that weak access controls are the most easily exploited vulnerability in AI systems. Without strong access restrictions, adversaries can directly query, extract, manipulate, or overload models, leading to data leakage or compromised outputs. While inaccurate generalizations, DoS vulnerabilities, or susceptibility to input manipulation are serious, they typically require more effort or specific conditions. Weak access control provides the most direct and immediate entry point for attackers. As such, it is identified as the most easily exploited vulnerability.

NEW QUESTION # 288
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?

A. Prohibit users from manipulating agent behavior
B. Allow users to configure the agent for productivity
C. Limit human review of AI decisions
D. Validate agent compliance with output restrictions

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 # 289
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