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ISACA AAISM Exam is Easy with Our Verified AAISM Exam Quiz: ISACA Advanced in AI Security Management (AAISM) Exam

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ISACA AAISM Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q95-Q100):

NEW QUESTION # 95
Which of the following BEST describes an adversarial attack on an AI model?

A. Providing inputs that mislead the AI model into incorrect predictions
B. Attacking the underlying hardware of the AI system
C. Conducting denial-of-service (DoS) attacks against AI APIs
D. Reverse engineering the AI model using social engineering techniques

Answer: A

Explanation:
In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged. Hardware compromise (A) and DoS (D) are system-level attacks, not adversarial ML per se. Social engineering to obtain model information (C) is an information-gathering or security breach vector, distinct from input-space adversarial manipulation.
References:AI Security Management (AAISM) Body of Knowledge: Taxonomy of AI Threats- Adversarial Examples and Evasion; Distinction from System/Network Attacks.AAISM Study Guide:
Definitions and Threat Models in Adversarial ML; Attack-Defense Mapping for Model Predictions.

NEW QUESTION # 96
Which of the following involves documenting and monitoring the complete journey of data as it flows through an AI system?

A. Origin
B. Processing
C. Lineage
D. Transformation

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 # 97
An organization is implementing AI agent development across engineering teams. What should AI-specific training focus on?

A. Output moderation, hallucination handling, policy alignment
B. Dataset bias, explainability, fairness
C. Prompt injection, agent memory control, insecure tool execution
D. API abuse, data leakage, third-party plug-in risk

Answer: C

Explanation:
AAISM states that AI agent security training should focus on the unique risks of agentic systems, which include:
* prompt injection
* memory control and context hijacking
* unsafe tool execution (agents triggering unauthorized actions)
These risks are specific to autonomous or semi-autonomous AI agents.
Bias, fairness (B) and output moderation (C) are important but not the most critical for agent security. API abuse and plug-in risk (D) matter but are secondary.
References: AAISM Study Guide - Agentic AI Security; Prompt Injection and Tool Execution Risks.

NEW QUESTION # 98
When evaluating a new AI tool for intrusion prevention, which is MOST important to ensure fit within the existing program architecture?

A. Ensure automated response orchestration
B. Confirm tool capabilities align with control objectives
C. Prioritize real-time anomaly detection
D. Select a tool that integrates with the SIEM

Answer: B

Explanation:
AAISM stresses that AI tools must align with the organization's existing control objectives and governance requirements, ensuring consistency with risk management, detection philosophy, and operational processes.
Integration with SIEM (D) is important but secondary. Anomaly detection (B) is a feature, not an architectural requirement. Automated orchestration (A) is optional.
References: AAISM Study Guide - AI Security Architecture & Control Alignment.

NEW QUESTION # 99
The PRIMARY purpose of adopting and implementing AI architecture within an organizational AI program is to:

A. Provide a basis for identifying threats and vulnerabilities
B. Deploy fast and cost-efficient AI systems
C. Align AI system components with business goals
D. Ensure powerful and scalable AI systems

Answer: C

Explanation:
AAISM describes AI architecture as a strategic alignment framework, ensuring AI systems, data pipelines, governance processes, and capabilities match organizational business goals and regulatory requirements.
While threat identification (B) and scalability (D) are advantages, the primary purpose is business alignment.
Option A is not a core architectural objective.
References: AAISM Study Guide - AI Architecture and Business Alignment.

NEW QUESTION # 100
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