ISACA AAISM Frequent Updates - AAISM Lab Questions

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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Technologies and Controls38%- Security monitoring, testing, and continuous assurance for AI systems
- Data management and protection controls for AI
- Privacy, ethics, trust, and safety controls
- AI lifecycle security: model selection, training, validation, and deployment
- AI security architecture and secure design principles
Topic 2: AI Governance and Program Management31%- AI security program development and management
- AI asset and data lifecycle management
- AI security policies, standards, and procedures
- AI governance frameworks and alignment with business objectives
- Business continuity and incident response for AI systems
Topic 3: AI Risk Management31%- Regulatory compliance and ethical considerations in AI
- AI risk assessment, thresholds, and treatment strategies
- AI vendor and supply chain risk management
- AI threat and vulnerability management

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q299-Q304):

NEW QUESTION # 299
When integrating AI for innovation, which of the following can BEST help an organization manage security risk?

Answer: B

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.


NEW QUESTION # 300
An organization's HR chatbot was discovered generating fake employee details using a third- party API. Which would be the information security team's BEST recommendation?

Answer: B

Explanation:
The issue stems from the chatbot relying on an external API that is producing unreliable outputs.
Limiting or restricting communication with the third-party API reduces exposure to untrusted responses and prevents further generation of incorrect or fabricated employee information, directly mitigating the risk.


NEW QUESTION # 301
When creating a use case for an AI model that provides sensitive decisions affecting end users, which of the following is the GREATEST benefit of using model cards?

Answer: A

Explanation:
AAISM highlights that model cards are a governance tool designed to document ethical considerations, limitations, fairness constraints, data sources, and suitability of use cases for AI models--especially when they affect individuals' rights, opportunities, or access to services.
Their greatest value is providing transparency and ethical clarity, ensuring stakeholders understand risks, bias considerations, and how decisions impact users.


NEW QUESTION # 302
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 # 303
Which attack type is MOST likely to cause model drift?

Answer: A

Explanation:
AAISM defines data poisoning as directly capable of causing model drift because corrupted training data shifts the statistical distribution, leading to degraded or unsafe performance.
Model stealing (A) extracts model behavior but does not cause drift. Perfect knowledge (B) is an attacker capability, not an attack causing drift. Membership inference (D) attacks privacy, not performance.
References: AAISM Study Guide - Model Drift Causes; Data Poisoning Impact.


NEW QUESTION # 304
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