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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
An organization needs large data sets to perform application testing. Which of the following would BEST fulfill this need?

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

Answer: C

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 # 92
A military contractor discovered that its large language model (LLM) is at high risk of being targeted by advanced persistent threat (APT) actors seeking to exploit the model to access confidential information.
Which of the following attacks is the HIGHEST priority to protect against?

A. Data poisoning
B. Model inversion
C. Model distillation
D. Unauthorized tuning

Answer: B

Explanation:
AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs-directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario's stated objective-accessing confidential information-most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.
References:* AI Security Management (AAISM) Body of Knowledge: Model Security-Inference-Time Threats (Inversion, Membership Inference) and Confidentiality Risks* AAISM Study Guide: Leakage Mitigations-Regularization, Output Minimization/Calibration, Access Controls & Monitoring on Model Interfaces

NEW QUESTION # 93
Which BEST describes the role of model cards in AI solutions?

A. They automatically fine-tune AI models
B. They visualize AI model performance
C. They help developers create synthetic data
D. They document training data and AI model use cases

Answer: D

Explanation:
AAISM explains that model cards provide structured documentation about AI models, including:
- intended use cases
- training data characteristics
- ethical considerations
- known limitations
- risk factors
- performance benchmarks

NEW QUESTION # 94
An aerospace manufacturing company that prioritizes accuracy and security has decided to use generative AI to enhance operations. Which of the following large language model (LLM) adoption plans BEST aligns with the company's risk appetite?

A. Developing a private LLM to automate non-critical functions
B. Developing a public LLM to automate critical functions
C. Contracting LLM access from a reputable third-party provider
D. Purchasing an LLM dataset on the open market

Answer: A

Explanation:
AAISM recommends aligning AI adoption with organizational risk appetite by limiting blast radius, protecting sensitive data, and staging adoption in lower-risk domains first. Building a private LLM for non-critical functions preserves data control, enables tighter governance (access control, logging, evaluation), and confines any model errors away from safety- or mission-critical operations. A public LLM for critical functions (A) is misaligned with a high-assurance posture; buying open-market datasets (B) raises provenance and licensing risk; third-party access (C) can be appropriate but still introduces vendor/visibility limits and data residency concerns that may not meet aerospace security needs.
References: AI Security Management (AAISM) Body of Knowledge - Risk Appetite Mapping to AI Use Cases; Criticality Segmentation; Data Control & Deployment Models. AAISM Study Guide - Phased Adoption for High-Assurance Environments; Private vs. Hosted LLM Trade-offs; Governance, Evaluation, and Containment Patterns.

NEW QUESTION # 95
An AI system that supports critical processes has deviated from expected performance and is producing biased outcomes. Which of the following is the BEST course of action?

A. Activate the model kill switch
B. Retrain the model with a new and expanded dataset
C. Conduct audits of the data and the model
D. Perform a root cause analysis to identify mitigation steps

Answer: D

Explanation:
AAISM directs that when harmful or biased behavior is observed in a production AI system, the organization should enter a formal incident/variance handling workflow that begins with root cause analysis (RCA) to identify the source of deviation (data drift, concept drift, feature leakage, pipeline changes, control failures) and determine proportionate risk treatments. Immediate retraining (Option A) without RCA risks reinforcing the same bias; audits (Option C) are key activities within RCA rather than the action that frames the response; a kill switch (Option D) is reserved for conditions where risk exceeds the defined tolerances and immediate harm prevention is required.
References: AI Security Management (AAISM) Body of Knowledge - Incident Response & Post-Incident Improvement; Model Risk Treatment & Drift Management; Bias Detection and Remediation Governance.

NEW QUESTION # 96
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