On-Premises LLM for Enterprise AI Assistants: What to Consider?
It is possible to leverage artificial intelligence without exposing corporate data. However, not without authorization, validation, and auditing.
Locally running Large Language Models (LLMs) allow sensitive data to be processed without being sent to third-party services. However, running the model on your own server does not automatically mean a secure system.
The assistant must not see data that the user cannot see
In a RAG (Retrieval-Augmented Generation) infrastructure, the documents provided to the model must be filtered according to the authorization of the querying user. Otherwise, the assistant may summarize and present information that an employee cannot access.
Models make mistakes
Language models can generate persuasive yet incorrect answers. In critical use cases such as financial analysis or report summarization, source attribution and human-in-the-loop validation steps must be part of the design.
Every interaction is recorded
Audit logs must track which user asked which question with which data, and these logs themselves must be protected with access controls.