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Isaca AAISM Exam Questions
- 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.
Free Isaca AAISM Exam Actual Questions
Note: Premium Questions for AAISM were last updated On Jul. 10, 2026 (see below)
What is the GREATEST benefit of performing AI security risk assessments?
AAISM emphasizes that the primary value of AI security risk assessments is prioritizing risks based on likelihood, impact, and business relevance.
Updating the register (A) is administrative. Privacy controls (B) are one category of mitigation. Funding (D) is possible but not the primary purpose.
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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?
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.
An organization deploying an LLM is concerned input manipulations could compromise security. What is the MOST effective way to determine an acceptable risk threshold?
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.
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Which of the following is MOST important to ensure security throughout the AI data life cycle?
AAISM emphasizes data lineage, provenance tracking, and inventory completeness as essential controls to ensure data security and accountability across all AI data life-cycle phases. This enables detection of unauthorized modifications, improper use, and compliance violations.
Periodic reviews (B) are necessary but insufficient without lineage. Restricting third-party use (C) is one control but not comprehensive. Open-source model choice (A) does not secure data.
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The PRIMARY ethical concern of generative AI is that it may:
AAISM materials emphasize that the primary ethical concern with generative AI is the risk to information integrity. Generative models can create content that appears authentic but is fabricated, misleading, or manipulated. This undermines trust in information ecosystems and can have wide-reaching social, legal, and organizational impacts. While confidentiality breaches and bias are concerns, they are not the central ethical issue inherent to generative models. Availability is less relevant in this context. The most pressing concern is that generative AI may compromise the integrity of information.
AAISM Study Guide -- AI Risk Management (Ethical Risks of Generative AI)
ISACA AI Security Management -- Integrity Concerns in Generative Systems
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