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ISACA AAISM Exam Syllabus Topics:

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
Which of the following is the PRIMARY benefit of incorporating an AI component into an organization's security program?

Answer: C

Explanation:
Incorporating an AI component ensures that security efforts address AI-specific threats and vulnerabilities while aligning with established cybersecurity practices, strengthening the overall security posture.


NEW QUESTION # 32
A backdoor into an AI model was identified through an investigation into a specific set of fraudulent transactions. Which of the following is the BEST course of action?

Answer: B

Explanation:
A backdoor indicates the model itself has been compromised. Reverting to a previously trusted version removes the malicious modification and restores integrity, providing the quickest and most effective way to eliminate the backdoor before further remediation steps are taken.


NEW QUESTION # 33
The PRIMARY goal of data poisoning attacks is to:

Answer: C

Explanation:
AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine-tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production. While poisoning occurs during development/training (C), its primary objective is the downstream integrity impact on predictions/outputs (D). Options A and B relate to confidentiality threats (e.g., inversion or leakage), not poisoning.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Integrity Threats-data poisoning aims and effects; supply-side data controls* AAISM Study Guide: Risk scenarios for poisoning; mitigations via data provenance checks, robust training, and anomaly detection


NEW QUESTION # 34
Which of the following is the BEST method to uncover known vulnerabilities in an AI-based web application that has been recently deployed?

Answer: C


NEW QUESTION # 35
An organization develops and implements an AI-based plug-in for users that summarizes their individual emails. Which of the following is the GREATEST risk associated with this application?

Answer: D

Explanation:
According to AAISM risk management guidance, the greatest risk in AI applications handling personal communication data is inadequate parameter controls, which may allow unintended access, manipulation, or leakage of sensitive information. Plug-ins that interact with emails must enforce strict parameter validation and security restrictions to prevent unauthorized or manipulated inputs. While vulnerability scanning, format incompatibility, and API rate limiting are valid concerns, they are secondary. The primary risk is a lack of strong parameter controls that could expose sensitive content.


NEW QUESTION # 36
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