Ensuring Privacy with LLMs

Developers integrating large language models into production systems face a growing tension. The more context you send to an LLM, the better the output, but the greater the surface area for data leakage, unauthorized retention, and compliance risk. Privacy is not a single feature you toggle on. It is a stack of decisions covering transport encryption, prompt hygiene, provider policies, and infrastructure isolation. This article breaks down a practical, defense-in-depth approach to keeping sensitive data safe when using third-party inference APIs, and shows where Oxlo.ai fits into a privacy-first architecture.

Start with a Threat Model

Before choosing tools, map what can actually