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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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AAISM Exam Question | AAISM Exam Exercise

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q150-Q155):

NEW QUESTION # 150
Which of the following is the MOST important consideration for an organization that has decided to adopt AI to leverage its competitive advantage?

Answer: B

Explanation:
AAISM's governance guidance emphasizes that adopting AI for competitive advantage must begin with a comprehensive strategic roadmap for integration. This roadmap aligns AI adoption with business objectives, sets priorities, defines milestones, and ensures coordination across functions. Risk management, training, and tool procurement are essential, but they are tactical steps that follow once the strategic direction is defined.
Without a roadmap, adoption becomes fragmented and risks misalignment with business strategy. The most important consideration at the adoption stage is therefore creating a strategic integration roadmap.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Strategy and Roadmapping) AI Security Management Study Guide - Business Alignment of AI Initiatives


NEW QUESTION # 151
Which of the following is the MAIN objective of the operational phase of AI life cycle management?

Answer: A

Explanation:
AAISM identifies the operational phase as the stage where the AI system is actively deployed and requires continuous monitoring of performance, drift, security, and reliability.
Business alignment (B) belongs to planning and design. Optimization (A) and user feedback (D) are part of development or improvement cycles, not primary operational objectives.
References: AAISM Study Guide - AI Life Cycle Management; Operational Monitoring Requirements.


NEW QUESTION # 152
Which of the following methods provides the MOST effective protection against model inversion attacks?

Answer: D

Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.


NEW QUESTION # 153
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?

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.


NEW QUESTION # 154
A regulator warns of increased risk of AI re-identification attacks on anonymized datasets. What should the information security manager do FIRST?

Answer: A

Explanation:
AAISM states that anonymization is not permanent and may be reversible through re-identification attacks.
The first action should be to evaluate and measure the actual privacy risk through:
* adversarial re-identification testing
* privacy audits
* monitoring for misuse
This provides the factual basis needed before making destructive or operational decisions.
Access control (D) is important but not the FIRST step. Deleting datasets (B) is premature. Assuming anonymization is permanent (A) violates AI privacy principles.
References: AAISM Study Guide - Privacy Risk, Re-Identification Threats, Adversarial Testing.


NEW QUESTION # 155
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