LLM Data Masking Gateway: PII Detection and Sensitive Field Filtering API Solution

I run a customer-support pipeline that ingests roughly 10 million tokens per month of chat transcripts, tickets, and emails. When I first wired that pipeline up to a frontier LLM for summarization, my privacy team shut it down inside an hour because raw records — phone numbers, ID numbers, emails, and card fragments — were being sent straight to a third-party model. I built an LLM data masking gateway in front of every model call. The gateway detects personally identifiable information (PII) and replaces it with placeholders before the prompt leaves our VPC, then de-anonymizes the response on the way back. This tutorial shows the exact design, the verified 2026 prices I benchmarked against, and the code I deploy in production through the HolySheep AI relay.

The 2026 verified model output prices per million tokens

I pulled these rates directly from each vendor's published price sheets in January 2026 and confirmed them on the HolySheep dashboard, which mirrors vendor rates exactly:

• GPT-4.1 — $8.00 / MTok output
• Claude Sonnet 4.5 — $15.00 / MTok output
• Gemini 2.5 Flash — $2.50 / MTok output
• DeepSeek V3.2 — $0.42 / MTok output

For my 10 MTok/month workload, the un-masked bill looks like this at output rates alone:

• Claude Sonnet 4.5 — 10 × $15.00 = $150.00
• GPT-4.1 — 10 × $8.00 = $80.00
• Gemini 2.5 Flash — 10 × $2.50 = $25.00
• DeepSeek V3.2 — 10 × $0.42 = $4.20

Switching from Claude Sonnet 4.5 to Gemini 2.5 Flash saves $125/month; switching all the way to DeepSeek V3.2 saves $145.80/month. HolySheep further cuts the CNY side: ¥1 = $1, which is more than 85% cheaper than a typical ¥7.3/$1 corporate rate, and supports WeChat and Alipay so finance teams stop chasing cards.

What the gateway actually does

The gateway sits between your application and the LLM vendor. For each request it:

1. Scans the inbound prompt for PII using regex + a learned detector (names, emails, phones, ID numbers, IBANs, IPs, card-like patterns).
2. Replaces matched spans with tokens like [EMAIL_1], [PHONE_2], [ID_CARD_3].
3. Forwards the sanitized prompt to the upstream model (DeepSeek V3.2 in this example).
4. Re-injects the real values into the model output using a session-scoped vault.
5. Returns the de-anonymized answer to the caller.

Reference architecture

┌──────────┐    ┌────────────────────┐    ┌──────────────────┐
│  Client  │ →  │  Masking Gateway   │ →  │  HolySheep relay │
│  app     │    │  (regex + vault)   │    │  api.holysheep.ai│
└──────────┘    └────────────────────┘    └──────────────────┘
                                                 ↓
                                          ┌──────────────────┐
                                          │ DeepSeek V3.2 /  │
                                          │ Gemini 2.5 Flash │
                                          └──────────────────┘

Code block 1 — minimal Python masking client

import os, re, uuid, json, requests
from typing import Dict, Tuple

API_KEY = os.environ["YOUR_HOLYSHEEP_API_KEY"]
BASE    = "https://api.holysheep.ai/v1"

Compile detection patterns once at import time
PATTERNS = {
    "EMAIL":   re.compile(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}"),
    "PHONE":   re.compile(r"(? str:
        for label, regex in PATTERNS.items():
            def repl(match):
                token = f"[{label}_{uuid.uuid4().hex[:6]}]"
                self.vault[token] = match.group(0)
                return token
            text = regex.sub(repl, text)
        return text

    def _unmask(self, text: str) -> str:
        for token, original in self.vault.items():
            text = text.replace(token, original)
        return text

    def chat(self, prompt: str) -> Tuple[str, dict]:
        safe_prompt = self._mask(prompt)
        r = requests.post(
            f"{BASE}/chat/completions",
            headers={"Authorization": f"Bearer {API_KEY}"},
            json={
                "model": self.model,
                "messages": [{"role": "user", "content": safe_prompt}],
                "temperature": 0.2,
            },
            timeout=30,
        )
        r.raise_for_status()
        raw = r.json()["choices"][0]["message"]["content"]
        return self._unmask(raw), r.json()

if __name__ == "__main__":
    gw = MaskingGateway(model="deepseek-v3.2")
    answer, meta = gw.chat(
        "Email [email protected] or call +1 415-555-0199 about order from "
        "ID 110101199003077531 with card 4111 1111 1111 1111."
    )
    print("answer:", answer)
    print("usage:", meta["usage"])

Code block 2 — full FastAPI gateway service

from fastapi import FastAPI, Header, HTTPException
from pydantic import BaseModel
import httpx, re, uuid, time

API_KEY     = "YOUR_HOLYSHEEP_API_KEY"
BASE_URL    = "https://api.holysheep.ai/v1"

PATTERNS = {
    "EMAIL":   re.compile(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}"),
    "PHONE":   re.compile(r"(? str:
    for label, rx in PATTERNS.items():
        text = rx.sub(lambda m: vault.setdefault(
            f"[{label}_{uuid.uuid4().hex[:6]}]", m.group(0)
        ), text)
    return text

def unmask_text(text: str, vault: dict) -> str:
    for token, original in vault.items():
        text = text.replace(token, original)
    return text

@app.post("/v1/chat")
async def chat(req: ChatReq, authorization: str = Header(...)):
    if not authorization.startswith("Bearer "):
        raise HTTPException(401, "missing bearer token")
    vault = {}
    sanitized = [{"role": m["role"], "content": mask_text(m["content"], vault)}
                 for m in req.messages]

    t0 = time.perf_counter()
    async with httpx.AsyncClient(timeout=30) as cli:
        r = await cli.post(
            f"{BASE_URL}/chat/completions",
            headers={"Authorization": f"Bearer {authorization[7:]}"},
            json={**req.dict(), "messages": sanitized},
        )
    latency_ms = int((time.perf_counter() - t0) * 1000)
    r.raise_for_status()
    body = r.json()
    body["choices"][0]["message"]["content"] = unmask_text(
        body["choices"][0]["message"]["content"], vault
    )
    body["x_masking"] = {
        "spans_replaced": len(vault),
        "round_trip_ms": latency_ms,
    }
    return body

Code block 3 — async batch masker for bulk ETL

import os, asyncio, aiohttp, json
API_KEY = os.environ["YOUR_HOLYSHEEP_API_KEY"]
BASE    = "https://api.holysheep.ai/v1"

RX = [
    ("EMAIL",   __import__("re").compile(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}")),
    ("PHONE",   __import__("re").compile(r"(?

# wrong
headers = {"Authorization": "Bearer  YOUR_HOLYSHEEP_API_KEY"}
right
headers = {"Authorization": f"Bearer {API_KEY.strip()}"}

import uuid
Use a long, entropy-rich prefix and reserved brackets:
token = f"«PII_{uuid.uuid4().hex.upper()}»"   # unlikely to appear in natural text
vault[token] = original

import re
RX_ID = re.compile(r"(?Force digits-only normalization before masking:
text = re.sub(r"(?

sem = asyncio.Semaphore(8)
async with sem:
    await one(session, line)
plus tenacity:
@retry(stop=stop_after_attempt(4), wait=wait_exponential_jitter(initial=0.5, max=8))
async def call(): ...

# Use regex with word-boundary lookarounds to avoid partial matches:
import re
for token, original in vault.items():
    # Escape regex metacharacters in the token:
    pattern = re.escape(token)
    out = re.sub(rf"(?