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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 (Q13-Q18):

NEW QUESTION # 13
When documenting information about machine learning (ML) models, which of the following artifacts BEST helps enhance stakeholder trust?

A. Hyperparameters
B. Model card
C. Data quality controls
D. Model prototyping

Answer: B

Explanation:
Themodel cardis a governance artifact that communicatesintended use, performance characteristics, limitations, fairness considerations, and ethical notesof an ML model.
AAISM governance materials highlight thatstakeholder trustcomes from transparency and explainability.
While hyperparameters and data quality controls are technical details, they lack stakeholder-facing clarity.
Model prototyping is part of development but not a governance record.
Model cards are explicitly recommended to:
* Provideexplainabilityand context for decision-making.
* Demonstrate governance, transparency, and compliance.
* Enable stakeholders (regulators, auditors, business leaders) to trust the system.
Therefore, themodel cardis the artifact that best enhances trust.

NEW QUESTION # 14
The PRIMARY ethical concern of generative AI is that it may:

A. Cause information integrity issues
B. Produce unexpected data that could lead to bias
C. Cause information to become unavailable
D. Breach the confidentiality of information

Answer: A

Explanation:
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.
References:
AAISM Study Guide - AI Risk Management (Ethical Risks of Generative AI) ISACA AI Security Management - Integrity Concerns in Generative Systems

NEW QUESTION # 15
An organization needs large data sets to perform application testing. Which of the following would BEST fulfill this need?

A. Incorporating data from search content
B. Performing AI data augmentation
C. Reviewing AI model cards
D. Using open-source data repositories

Answer: D

Explanation:
According to AAISM study guidance, the most direct and effective way to obtain large volumes of diverse data for application testing is through open-source data repositories. These repositories provide freely available, well-documented, and often standardized data that supports testing and benchmarking in a compliant manner. Model cards document AI behavior but do not provide data. Incorporating search content may introduce legal, privacy, and quality risks. Data augmentation is useful for expanding existing sets but does not provide the breadth or size required when starting with insufficient data. The recommended best practice for sourcing large testing datasets is therefore the use of open-source repositories.
References:
AAISM Study Guide - AI Technologies and Controls (Data Sources and Testing Practices) ISACA AI Security Management - Data Governance and Compliance in AI Testing

NEW QUESTION # 16
A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:

A. Evasion attacks
B. Poisoning attacks
C. Model exfiltration
D. Membership inference

Answer: A

Explanation:
The AAISM study framework describes evasion attacks as attempts to manipulate or probe a trained model during inference by using crafted inputs that appear normal but cause the system to generate inconsistent or erroneous outputs. Contradictory results from nearly identical queries are a typical symptom of evasion, as the attacker is probing decision boundaries to find weaknesses. Poisoning attacks occur during training, not inference, while membership inference relates to exposing whether data was part of the training set, and model exfiltration involves extracting proprietary parameters or architecture. The clearest indication of contradictory outputs from similar queries therefore aligns directly with the definition of evasion attacks in AAISM materials.
References:
AAISM Study Guide - AI Technologies and Controls (Adversarial Machine Learning and Attack Types) ISACA AI Security Management - Inference-time Attack Scenarios

NEW QUESTION # 17
Which of the following controls BEST mitigates the inherent limitations of generative AI models?

A. Classifying and labeling AI systems
B. Ensuring human oversight
C. Adopting AI-specific regulations
D. Reverse engineering the models

Answer: B

Explanation:
The AAISM governance framework emphasizes that the inherent limitations of generative AI-including hallucinations, bias, and unpredictability-are best mitigated by human oversight. Human-in-the-loop review ensures that outputs are validated before being used in sensitive or high-risk contexts. Regulatory adoption, system classification, and reverse engineering all play supporting roles but do not directly safeguard against the model's inherent unpredictability. Governance best practices highlight human oversight as the critical safeguard.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Human Oversight and Accountability) AI Security Management Study Guide - Mitigating Generative AI Limitations

NEW QUESTION # 18
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ISACA AAISM Pdf Exam Dump - Latest Braindumps AAISM Ppt

ISACA AAISM Exam Syllabus Topics:

Latest Braindumps ISACA AAISM Ppt, AAISM Latest Exam Format

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q13-Q18):

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