Most AI privacy leaks happen before the model call. I built a Python layer to mask PII first

I kept seeing teams obsess over prompt quality and model choice, but sending raw customer data straight to LLM APIs. So I built a small Python package called ShieldPrompt to handle one boring but critical thing: mask sensitive data before it leaves your app, then restore it in the final response. The flow is simple:

1. Detect PII (regex by default, optional NER)
2. Replace with tokens like [EMAIL_ADDRESS_1]
3. Send masked text to LLM
4. Unmask final response using a per-request vault What I wanted was flexibility, so I added multiple integration points:

• Decorator: @mask_pii(...) for drop-in function wrapping
• Core engine: direct Shield().mask() / unmask()
• FastAPI middleware: masks request JSON string fields + unmasks response text
• CLI for mask/unmask/inspect workflows
• MCP server tools for agent workflows A couple implementation details that mattered a lot:
• Right-to-left replacement while masking (prevents index corruption)
• Length-sorted token restore while unmasking (prevents partial token collisions)
• Context-local vault isolation for concurrent requests
• Graceful fallback to regex-only if NER deps aren’t installed It’s open source and still early, so I’d love practical feedback from people running LLM apps in production: How are you currently handling PII in prompts/responses without adding a ton of complexity?