Quiz ISACA - AAISM - Newest Valid ISACA Advanced in AI Security Management (AAISM) Exam Test Blueprint

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
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 (Q141-Q146):

NEW QUESTION # 141
Which of the following strategies BEST ensures generative AI tools do not expose company data?

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

Answer: A

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 # 142
Which BEST addresses hallucination risk in AI systems?

A. Human oversight
B. Automated output validation
C. Recursive chunking
D. Content enrichment

Answer: A

Explanation:
AAISM states that human oversight is the strongest control for hallucination risks, especially in high-impact decisions. Humans validate outputs, correct errors, and override unsafe model responses.
Automated validation (C) helps but cannot fully detect hallucinations. Chunking (B) improves information handling but not hallucination mitigation. Content enrichment (D) does not reduce hallucinations.
References: AAISM Study Guide - Human-in-the-Loop (HITL) Controls; Hallucination Mitigation.

NEW QUESTION # 143
Which of the following is the MOST effective way to identify and address security risk in an AI model?

A. Conduct threat modeling to identify vulnerabilities and possible attack methods
B. Assign staff to review AI model outputs for accuracy
C. Encrypt the training data and model parameters to prevent unauthorized access
D. Add more data to the model to increase its accuracy and reduce errors

Answer: A

Explanation:
AI/ML threat modeling is the most effective structured method to both identify and address model security risks. It systematically surfaces attack classes (poisoning, evasion, membership inference, model extraction, inversion), maps system-specific attack surfaces (data pipelines, feature stores, training artifacts, inference APIs), and drives prioritized mitigations (ingestion validation, robust training, rate-limiting, watermarking, differential privacy, monitoring, red teaming). Output spot-checking (A) finds errors but not security vulnerabilities; encryption (C) protects confidentiality but does not reveal threats or mitigate inference-time attacks; adding data (D) may improve accuracy but does not target adversarial risk.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Risk Identification & Threat Modeling; Attack Surface Analysis for ML; Risk Treatment Planning. AAISM Study Guide - Evasion
/Poisoning/Extraction Controls; Mapping Risks to Controls; Validation and Assurance Activities.

NEW QUESTION # 144
Which BEST describes the role of model cards in AI solutions?

A. They help developers create synthetic data
B. They automatically fine-tune AI models
C. They document training data and AI model use cases
D. They visualize AI model performance

Answer: C

Explanation:
AAISM explains that model cards provide structured documentation about AI models, including:
* intended use cases
* training data characteristics
* ethical considerations
* known limitations
* risk factors
* performance benchmarks
They are not visualization tools (A), do not create synthetic data (C), and do not tune models (D).
References: AAISM Study Guide - AI Transparency & Model Cards.

NEW QUESTION # 145
An organization plans to implement a new AI system. Which of the following is the MOST important factor in determining the level of risk monitoring activities required?

A. The organization's risk appetite
B. The organization's compensating controls
C. The organization's number of AI system users
D. The organization's risk tolerance

Answer: D

Explanation:
AAISM risk management guidance clarifies that the organization's risk tolerance is the most important factor in determining how much monitoring is needed. Risk tolerance specifies the amount of risk the organization is willing to accept and defines the threshold for triggering monitoring or mitigation activities. Risk appetite is broader and strategic, while tolerance sets the operational limits. The number of users may influence scale, and compensating controls may affect resilience, but neither dictates monitoring intensity as directly as risk tolerance.
References:
AAISM Study Guide - AI Risk Management (Risk Appetite vs. Tolerance)
ISACA AI Security Management - Monitoring Based on Risk Tolerance

NEW QUESTION # 146
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Quiz ISACA - AAISM - Newest Valid ISACA Advanced in AI Security Management (AAISM) Exam Test Blueprint

ISACA AAISM Exam Syllabus Topics:

Superb AAISM Exam Questions Supply You Marvelous Learning Dumps - Itcerttest

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q141-Q146):

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