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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 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 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 (Q92-Q97):

NEW QUESTION # 92
Which approach should an organization prioritize to effectively verify the security of its AI models?

A. Automating vulnerability identification
B. Using standard penetration testing methods
C. Testing team competencies in IT threat mitigation
D. Developing a testing strategy including AI-specific threat modeling and adversarial attack simulations

Answer: D

Explanation:
The AAISM standard explicitly states that traditional penetration tests alone are insufficient for AI systems.
Effective AI security testing requires:
* AI-specific threat modeling (e.g., data poisoning, prompt injection, model theft)
* Adversarial attack simulations (white-box, black-box, gradient-based attacks)
* Evaluation of robustness and manipulation resistance
Option B captures these requirements precisely.
Options A, C, and D do not address AI-specific attack vectors.
References: AAISM Study Guide - AI Security Testing and Adversarial Evaluation.

NEW QUESTION # 93
Which of the following mitigation control strategies would BEST reduce the risk of introducing hidden backdoors during model fine-tuning via third-party components?

A. Performing threat modeling and integrity checks
B. Disabling runtime logs during model training
C. Leveraging open-source models and packages
D. Implementing unsupervised learning methods

Answer: A

Explanation:
The most effective way to reduce the risk of hidden backdoors entering during fine-tuning via third-party components is to apply supply-chain aware threat modeling and integrity verification across data, code, models, and dependencies. This includes SBOM/MBOM review, cryptographic signing and hash verification, controlled provenance of datasets and model weights, dependency pinning, secure artifact repositories, and pre-deployment security testing (including backdoor scans and evals). Merely preferring open-source (Option B) does not guarantee integrity; learning paradigm changes (Option C) are unrelated to supply-chain risk; and disabling logs (Option D) reduces forensic visibility and increases risk.
References:
AAISM Body of Knowledge: Secure AI Supply Chain; Model Provenance, Integrity and SBOM/MBOM Controls; Pre-deployment Security Testing and Backdoor/Poisoning Evals.
AAISM Study Guide: AI Threat Modeling (Attack Surfaces in Training/Fine-tuning); Third-Party/Vendor Component Assurance; Cryptographic Integrity and Artifact Governance.

NEW QUESTION # 94
A large financial services organization wants to use AI to create financial forecasts for clients.
Which of the following implementations is MOST likely to achieve the highest level of security?

A. Private
B. Retrieval-augmented generation (RAG)
C. Public
D. Large language model (LLM)

Answer: A

Explanation:
A private implementation keeps data, models, and processing within the organization's controlled environment, ensuring stronger protection of sensitive financial information, better compliance with regulatory requirements, and reduced exposure to external threats.

NEW QUESTION # 95
Which of the following involves documenting and monitoring the complete journey of data as it flows through an AI system?

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

Answer: B

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.

NEW QUESTION # 96
An organization is implementing AI agent development across engineering teams. What should AI-specific training focus on?

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

Answer: D

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 # 97
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