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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 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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ISACA AAISM Top Dumps & AAISM New Study Notes

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q301-Q306):

NEW QUESTION # 301
Which of the following would BEST ensure safe implementation of AI systems?

A. Establishment of an AI program steering committee to govern AI implementation.
B. Phased implementation with human-in-the-loop validation before full rollout.
C. Compliance with the AI risk management framework and applicable regulations.
D. Selection of AI projects that align with business objectives.

Answer: B

Explanation:
A phased implementation combined with human-in-the-loop validation allows the organization to monitor AI behavior, identify issues early, validate outputs, and maintain human oversight before full deployment. This approach most effectively ensures safe and controlled implementation of AI systems.

NEW QUESTION # 302
An organization deploying an LLM is concerned input manipulations could compromise security. What is the MOST effective way to determine an acceptable risk threshold?

A. Assess the business impact of known threats
B. Deploy real-time logging and monitoring
C. Implement a static threshold limiting LLM outputs
D. Restrict all inputs containing special characters

Answer: A

Explanation:
AAISM instructs that acceptable risk thresholds must be determined using business impact analysis. This aligns with the broader enterprise risk management principle of defining tolerances based on:
* potential harm
* regulatory exposure
* financial impact
* operational disruption
Monitoring (A) detects attacks but does not set thresholds. Blocking special characters (B) is unrealistic and overly restrictive. Static thresholds (D) ignore business context and practicality.
References: AAISM Study Guide - AI Risk Appetite and Threshold Determination.

NEW QUESTION # 303
An organization implementing a large language model (LLM) application notices significant and unexpected cost increases due to excessive computational resource usage. Which vulnerability is MOST likely in need of mitigation?

A. Excessive agency
B. System prompt leakage
C. Unbounded consumption
D. Sensitive information disclosure

Answer: C

Explanation:
AAISM highlights unbounded consumption (token/payment exhaustion, unmetered tool calls, prompt bombs) as a key LLM risk affecting cost and availability. Controls include request quotas, max tokens, rate- limits, budget guards, circuit breakers, and cost-aware routing. Excessive agency (A) relates to unsupervised actions; sensitive disclosure (B) and prompt leakage (C) are confidentiality risks, not primary drivers of runaway compute spend.
References: AI Security Management (AAISM) Body of Knowledge - LLM Risk Taxonomy (Abuse & Cost Risks); Guardrails: Rate-Limiting, Quotas, and Budget Controls; Resilience and Cost-Containment Patterns.

NEW QUESTION # 304
During the creation of a new large language model (LLM), an organization procured training data from multiple sources. Which of the following is MOST likely to address the CISO's security and privacy concerns?

A. Data discovery
B. Data augmentation
C. Data minimization
D. Data classification

Answer: C

Explanation:
AAISM guidance highlights data minimization as a critical practice for addressing both security and privacy concerns. By ensuring that only the minimum necessary data is collected and retained, the organization reduces the risk of sensitive information being exposed or misused during training. Data augmentation expands data but does not mitigate privacy risk. Classification organizes data but does not limit exposure.
Data discovery helps locate sources but does not directly reduce risks. The control that directly aligns with privacy-by-design principles is data minimization.
References:
AAISM Exam Content Outline - AI Risk Management (Data Privacy and Minimization) AI Security Management Study Guide - Privacy Safeguards in AI Training

NEW QUESTION # 305
A PRIMARY objective of responsibly providing AI services is to:

A. Ensure the confidentiality and integrity of data processed by AI models
B. Build trust for decisions and predictions made by AI models
C. Improve the ability of AI models to learn from new data
D. Enable AI models to operate autonomously

Answer: B

Explanation:
AAISM emphasizes that the primary objective of responsible AI is to establish and maintain trust in AI-driven decisions and predictions. Trust is achieved through transparency, accountability, fairness, and governance. While confidentiality and integrity are critical technical objectives, they are not the overarching purpose of responsible AI service provision. Autonomy and learning ability are features of AI, but without trust, adoption and compliance falter. The correct answer is that responsible AI services must focus on building trust in AI outcomes.

NEW QUESTION # 306
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