How to Build Privacy-Safe AI Integrations with MCP Servers and LLM Agents

You're building an AI integration — an MCP server, an agent pipeline, a business automation — and you hit a wall. Your prompts contain sensitive data. Guest names. Booking references. Patient records. Contract terms. API credentials. And you're about to pipe all of that directly into OpenAI or Anthropic's inference endpoints. The standard advice is: "just don't include sensitive data." But that's not how real workflows work. Context is what makes LLMs useful. Stripping the context breaks the feature. There's a better answer: scrub first, then send . This tutorial shows you how to add a privacy layer to any AI integration in 15 minutes. The Problem Every time you call openai.chat.completions.create() , that request includes: Your prompt — with whatever real data was in it Your API key (authenticated to you) Your IP address in the HTTP headers Timing and behavioral metadata — how often you call, what you ask about All of this hits OpenAI's infrastructure. It gets logged. It gets used for