A financial-services firm plans to roll out an AI assistant to help Linux administrators generate shell scripts and infrastructure-as-code templates. Corporate governance states that no customer account information or proprietary code may be processed by third-party providers outside the company's data centers. Which deployment strategy best satisfies this policy while following the Linux+ objective's guidance on private vs. public AI use?
Deploy an open-source LLM in an on-premises container that is reachable only through the internal VPN.
Permit use of any public AI service provided users delete the chat history after each session.
Integrate a browser plug-in that submits prompts to a public ChatGPT endpoint over HTTPS.
Subscribe to a SaaS LLM platform hosted by the vendor and enable model feedback to improve accuracy.
Deploying and operating an open-source large language model (LLM) inside the company's own network keeps all prompts, context, and generated code on infrastructure the organization controls. This approach allows security teams to audit access, maintain compliance with data-sovereignty regulations, and prevent model providers from retaining or retraining on the firm's data. Public SaaS or browser plug-ins send prompts to external servers where content can be logged or reused, creating the very data-exposure risk the policy forbids. Simply deleting chats after using a public service does not guarantee removal from the provider's backups or training pipeline, so it is insufficient for a strict governance requirement.
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CompTIA Linux+ XK0-006 (V8)
Automation, Orchestration, and Scripting
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