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NEW QUESTION # 398
Which of the following strategies BEST ensures generative AI tools do not expose company data?
Answer: A
Explanation:
AAISM prioritizes preventive controls at the point of use for generative AI, specifically input-governance and DLP controls that block or redact confidential, regulated, or high-risk data before it can be sent to external models. Audits, pre-deployment tests, and regulatory conformance are necessary but do not themselves prevent an employee from pasting sensitive content into prompts. Enforcing input restrictions, pattern-based redaction, policy-aware controls, and allow-lists for approved contexts provides the highest assurance of preventing exposure.
References:* AI Security Management (AAISM) Body of Knowledge: Data loss prevention for AI; prompt
/input controls; approved channels and guardrails for generative AI.* AI Security Management Study Guide: Preventive over detective controls for confidentiality; enterprise guardrails at prompt capture and egress points.
NEW QUESTION # 399
When evaluating a third-party AI service provider, which of the following master services agreement provisions is MOST critical for managing security risk?
Answer: A
Explanation:
The most material contractual control for reducing security and privacy risk in outsourced AI services is a data-use restriction that prohibits the provider from using customer data for model training (and from derivative model improvements) unless explicitly authorized. This prevents unintended secondary processing, model inversion exposure of proprietary data, unauthorized profiling, and downstream data proliferation across multi-tenant systems. AAISM positions third-party risk controls to prioritize data minimization, purpose limitation, confidentiality, and downstream controls; among common MSA provisions, data-use limitations directly constrain the provider's technical and organizational handling of sensitive inputs, making it the highest-impact risk-reducing clause. Query throttling (B) and logging (C) are useful operational controls but are secondary to legal/processing authority. Unlimited retraining (D) increases attack surface and cost without addressing the core risk of misuse of customer data.
References: AI Security Management™ (AAISM) Body of Knowledge - Third-Party & Supply-Chain Governance; Contractual Controls for AI Services; Data Minimization and Purpose Limitation. AAISM Study Guide - Procurement & MSA/DPA Clauses for AI; Provider Model Training and Data-Use Restrictions; Privacy & Confidentiality Safeguards in Outsourced AI.
NEW QUESTION # 400
An organization's business model relies on an AI application that handles sensitive customer information. From a governance perspective, which of the following is the MOST important consideration?
Answer: A
Explanation:
Leadership accountability is the most important governance consideration because governance requires clear ownership, oversight, and responsibility for how AI systems manage sensitive customer information, comply with regulations, and align with organizational risk tolerance and ethical standards.
NEW QUESTION # 401
Which of the following should be done FIRST when identifying and analyzing entry points, interfaces, and components of AI systems that malicious actors could exploit?
Answer: A
Explanation:
Understanding the system structure and how data moves between components is the foundational step in identifying potential attack surfaces. Data flow diagrams provide a clear view of entry points, interfaces, and interactions, enabling effective analysis of where malicious actors could exploit the system.
NEW QUESTION # 402
An organization is implementing AI agent development across engineering teams. What should AI-specific training focus on?
Answer: B
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 # 403
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