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ISACA AAISM Exam Syllabus Topics:

TopicDetails
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 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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q381-Q386):

NEW QUESTION # 381
An aerospace manufacturing company that prioritizes accuracy and security has decided to use generative AI to enhance operations. Which of the following large language model (LLM) adoption plans BEST aligns with the company's risk appetite?

Answer: C

Explanation:
AAISM recommends aligning AI adoption with organizational risk appetite by limiting blast radius, protecting sensitive data, and staging adoption in lower-risk domains first. Building a private LLM for non-critical functions preserves data control, enables tighter governance (access control, logging, evaluation), and confines any model errors away from safety-or mission-critical operations.


NEW QUESTION # 382
When integrating AI for innovation, which of the following can BEST help an organization manage security risk?

Answer: A

Explanation:
AAISM emphasizes that when introducing innovative AI systems, organizations reduce security and compliance risk by following a phased adoption approach. This allows incremental deployment, controlled testing, and gradual scaling while monitoring risks in real time. Re-evaluating risk appetite and evaluating compliance are important governance steps but do not directly mitigate risks during implementation. Seeking third-party advice can add expertise but does not provide the structured control that phased integration offers.
The most effective risk management approach for AI innovation is to adopt a phased rollout strategy.
References:
AAISM Exam Content Outline - AI Risk Management (Innovation and Risk Control) AI Security Management Study Guide - Phased Implementation Strategies


NEW QUESTION # 383
Which of the following controls BEST mitigates the risk of bias in AI models?

Answer: C

Explanation:
Bias in AI models primarily stems from limitations or imbalances in training data. The AAISM study materials emphasize that the most effective way to mitigate this risk is through diverse data sourcing strategies that ensure coverage across demographics, scenarios, and contexts. Access controls protect data security, not fairness. Data reconciliation ensures accuracy but does not address representational imbalance. Cryptographic hashing preserves integrity but has no impact on bias mitigation. To reduce systemic unfairness, the critical control is sourcing diverse and representative data.


NEW QUESTION # 384
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.
This aligns precisely with unexpected large compute bills.
Excessive agency (A) refers to unsafe autonomy, while disclosure (B) and prompt leakage (D) do not relate to compute overuse.
References: AAISM Study Guide - AI Abuse and Unbounded Consumption Risk.


NEW QUESTION # 385
Which of the following is BEST for analyzing true positives, true negatives, false positives, and false negatives produced by an AI model?

Answer: B

Explanation:
A confusion matrix is explicitly defined in AAISM as the framework used to interpret classification performance by listing:
* true positives
* true negatives
* false positives
* false negatives
Precision (B) and recall (D) are derived metrics that use parts of the matrix but do not show the full picture.
Hyperparameter tuning (A) is unrelated.
References: AAISM Study Guide - AI Model Evaluation Metrics; Confusion Matrix.


NEW QUESTION # 386
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