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ISACA AAISM Exam Syllabus Topics:

TopicDetails
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 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 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 (Q135-Q140):

NEW QUESTION # 135
Which AI data management technique involves creating validation and test data?

Answer: D

Explanation:
AAISM describes data splitting as the process of dividing datasets into:
- training
- validation
- test sets
This is essential for reducing overfitting and ensuring robust evaluation.


NEW QUESTION # 136
Which of the following recommendations would BEST help a service provider mitigate the risk of lawsuits arising from generative AI's access to and use of internet data?

Answer: D

Explanation:
The AAISM materials highlight that one of the primary legal risks with generative AI systems is the unauthorized use of copyrighted or intellectual property-protected data drawn from internet sources. To mitigate lawsuits, the most effective recommendation is to implement filtering logic that actively excludes data flagged for intellectual property risks before ingestion or generation. While disclosing compliance policies, appointing governance roles, or reviewing logs are supportive measures, they do not directly prevent the core liability of using restricted content. The study guide explicitly emphasizes that proactive filtering and data governance controls are the most effective safeguards against legal disputes concerning content origin.
References:
AAISM Exam Content Outline - AI Risk Management (Legal and Intellectual Property Risks) AI Security Management Study Guide - Generative AI Data Governance


NEW QUESTION # 137
Which of the following is the PRIMARY benefit of having robust model versioning and rollback capabilities for AI models?

Answer: B

Explanation:
Robust model versioning and rollback capabilities allow organizations to quickly restore a previously stable and validated model if a new version introduces degraded performance, instability, security issues, or operational failures. This directly mitigates the risk associated with model degradation or failure in production.


NEW QUESTION # 138
A viral video shows a blurry person making claims about a product safety issue. The video has random low- quality sections. This MOST likely represents what threat?

Answer: B

Explanation:
AAISM defines deepfakes as manipulated media where individuals appear in synthetic or altered video/audio.
Indicators include:
* blurred or inconsistent facial rendering
* mismatched frames
* low-quality or distorted transitions
These characteristics match the scenario provided.
Hallucinations (A) relate to model outputs, not video manipulation. Drift (B) affects model performance.
Poisoning (C) affects training data, not video content.
References: AAISM Study Guide - AI-Generated Media Threats; Deepfake Identification.


NEW QUESTION # 139
Which of the following involves documenting and monitoring the complete journey of data as it flows through an AI system?

Answer: C

Explanation:
Data lineage records and monitors the end-to-end journey of data-sources, movements, transformations, storage locations, uses, and dependencies-providing traceability, auditability, and accountability across the AI lifecycle. "Origin" is a single point (provenance), "transformation" is one step within the flow, and
"processing" is a general activity rather than a governance record of the entire path.
References: AI Security Management™ (AAISM) Body of Knowledge: Data Governance-Provenance and Lineage; AAISM Study Guide: Lineage Documentation, Traceability, and Audit Evidence.


NEW QUESTION # 140
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