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ISACA AAISM Exam Syllabus Topics:

Topic	Details
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 (Q235-Q240):

NEW QUESTION # 235
Which of the following MOST effectively minimizes the attack surface when securing AI agent components during their development and deployment?

A. Schedule periodic manual code reviews.
B. Deploy pre-trained models directly into production.
C. Implement compartmentalization with least privilege enforcement.
D. Consolidate event logs for correlation and centralized analysis.

Answer: C

Explanation:
The most effective strategy to minimize attack surfaces in AI agent security is to apply compartmentalization and least privilege enforcement.
AAISM control frameworks emphasize:
* Isolation of components (e.g., training, inference, data pipelines) to limit lateral movement.
* Principle of least privilege to restrict access only to what is required for function.
* Hardening AI pipelines through segmentation rather than relying solely on manual reviews or monitoring.
* Pre-trained models and log centralization are useful but do not directly reduce the attack surface.
* Manual code reviews are important but insufficient against runtime exploitation.
Thus, compartmentalization with least privilege enforcement is the most effective technical safeguard.

NEW QUESTION # 236
After an organization deploys an AI-based system, a regulator warns of increased risk related to AI re-identification attacks on anonymized data sets. Which of the following should the information security manager do FIRST?

A. Establish strong access controls for services using anonymized data
B. Immediately delete anonymized datasets and suspend AI services
C. Assume anonymization is permanent and continue regular business operations.
D. Implement a monitoring program including privacy audits and adversarial testing

Answer: D

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.

NEW QUESTION # 237
Which of the following is the MOST effective way to mitigate the effects of outdated data in a large language model (LLM)?

A. Deploying retrieval-augmented generation (RAG) data to the model
B. Replacing the model with a more complex model in the solution
C. Retraining the model periodically with an updated training dataset
D. Adding a new model trained with new information to the solution

Answer: A

Explanation:
Retrieval-augmented generation (RAG) enables the LLM to access current external information at query time instead of relying solely on static training data. This is the most effective way to mitigate outdated knowledge because it provides dynamically updated and context-relevant information without requiring full model retraining.

NEW QUESTION # 238
Which testing technique is BEST for determining how an AI model makes decisions?

A. Black box
B. Red team
C. Blue team
D. White box

Answer: D

Explanation:
AAISM indicates that white-box testing allows evaluators full visibility into:
* internal logic
* weights
* decision pathways
* model architecture
This makes it ideal for understanding how decisions are made.
Black box (B) provides no internal visibility. Red/blue team tests (A, D) focus on security, not decision mechanics.
References: AAISM Study Guide - AI Testing; Explainability Through White-Box Analysis.

NEW QUESTION # 239
Which of the following is the MOST likely cause of model drift?

A. Perfect knowledge
B. Membership inference
C. Model stealing
D. Data poisoning

Answer: D

Explanation:
Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data-centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high-likelihood driver of observable drift in production because it changes the effective data-generating process the model learns from.

NEW QUESTION # 240
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