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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 (Q332-Q337):

NEW QUESTION # 332
Which of the following is the MOST effective way to mitigate the risk of deepfake attacks?

A. Using a general-purpose large language model (LLM) to detect fraud
B. Limiting employee access to AI tools
C. Validating the provenance of the data source
D. Relying on human judgment for oversight

Answer: C

Explanation:
AAISM study content identifies validating the provenance of data sources as the most effective way to counter deepfake risks. Provenance validation ensures that content is authentic, verifiable, and traceable, preventing malicious synthetic media from being trusted as legitimate. Human oversight helps but cannot reliably detect sophisticated fakes. Limiting tool access reduces exposure but does not prevent external attacks. General-purpose LLMs are not optimized for fraud detection. The strongest control is verifying the origin and authenticity of data before acceptance.

NEW QUESTION # 333
A large organization has appointed the CISO to form an AI steering committee. Which of the following should be the CISO's FIRST course of action?

A. Benchmarking against peer organizations
B. Selecting an AI risk framework
C. Performing a gap analysis
D. Identifying the types of AI in use

Answer: D

Explanation:
The first step in establishing effective AI governance is identifying the types of AI currently in use across the organization. Understanding existing AI systems, use cases, and exposure is necessary before selecting frameworks, benchmarking, or conducting meaningful gap analyses.

NEW QUESTION # 334
The PRIMARY goal of data poisoning attacks is to:

A. manipulate the behavior of the model during development
B. compromise the confidentiality of model input data
C. compromise the confidentiality of output data from the model
D. undermine the integrity of the AI system's outputs

Answer: D

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 # 335
Employees are regularly using open-source generative AI without guidance. What should be the CISO's GREATEST concern?

A. Policy violations
B. Data leakage
C. Lack of monitoring
D. Model hallucinations

Answer: B

Explanation:
AAISM emphasizes that the primary and most severe risk of uncontrolled generative AI usage is data leakage, including:
- confidential data
- regulated personal data
- intellectual property
While hallucinations (A) are harmful, they do not cause direct data loss. Lack of monitoring (C) is a secondary risk. Policy violations (D) occur because of lack of controls but do not outweigh the impact of leaked sensitive data.

NEW QUESTION # 336
Which of the following presents the GREATEST challenge to gaining an understanding of how a deep learning system operates?

A. Insufficient output volumes and diversity to draw definitive conclusions
B. Ineffectiveness of traditional data flow mapping approaches
C. Complex non-linear relationships among features and parameters
D. Steep learning curve for interpretation of system testing results

Answer: C

Explanation:
Deep learning systems rely on highly complex non-linear relationships among large numbers of features, hidden layers, and parameters. These interactions make it difficult to understand, interpret, and explain how the model reaches specific decisions or outputs.

NEW QUESTION # 337
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