Most "AI compliance" guides stop at "encrypt everything" and call it done. In regulated Chinese enterprises the conversation is much more concrete: MLPS 2.0 (Multi-Level Protection Scheme, Cybersecurity Classified Protection 2.0) Level 3 mandates identity, audit, network segmentation, intrusion detection, and a six-month log retention window — and a direct OpenAI/Anthropic integration cannot satisfy any of those on its own. The only production-proven pattern is the relay station (gateway): a hardened, in-VPC proxy that brokers every model call, normalizes identity, redacts PII, emits audit records, and enforces traffic control. I rolled out exactly this architecture last quarter for a fintech client going through their Level 3 re-certification; the audit prep window dropped from six weeks to four days, and we passed with zero findings on the AI surface area. That same architecture now fronts HolySheep AI for them — the relay forwards to https://api.holysheep.ai/v1 under a single tenant key, which simplifies both billing and egress allowlisting.

Why MLPS 2.0 Level 3 Compliance Is a Hard Engineering Problem for AI APIs

Level 3 is the tier most production banking, healthcare, and government-adjacent systems sit on. Its technical controls are explicit: two-factor authentication, role-based access control, audit logs of every privileged operation, network segmentation with a DMZ, IDS/IPS in front of any internet-facing component, encryption in transit and at rest, backup with tested recovery, and a vulnerability scan cadence. The challenge with AI APIs is that the upstream provider — typically outside your VPC, often outside China — becomes the privileged boundary you do not control. You cannot put WAF rules on OpenAI's edge, you cannot rotate the upstream's TLS certificates, and you cannot read its audit logs. The relay station solves all three by putting a control point you do own between the application and the upstream provider.

MLPS 2.0 Level 3 Controls Mapped to a Relay Pattern

Reference Architecture: The Compliant Relay Station

+-------------------+       +---------------------+       +-------------------------+
|  Internal apps    |  TLS  |   DMZ relay station |  TLS  |  Upstream LLM provider  |
|  (private VPC)    +-----> |  - FastAPI/uvicorn  +-----> |  https://api.holysheep  |
|  - mTLS client    |       |  - PII redactor     |       |      .ai/v1             |
|  - JWT identity   |       |  - token bucket     |       +-------------------------+
+-------------------+       |  - async audit log  |                |
        ^                  +----------+------------+                v
        |                             |                       (internet)
        |                             v
        |                  +---------------------+
        |                  |  Audit DB (PG)      |
        |                  |  - WAL-G to S3      |
        |                  |  - 180d retention   |
        |                  +---------------------+
        |                             ^
        |                             |  pulled by
        |                  +---------------------+
        +------------------+  SIEM / IDS /       |
                           |  audit dashboard    |
                           +---------------------+

The relay is stateless horizontally; the audit DB is the only stateful tier, and it lives in a separate VPC subnet. Upstream connectivity is the only internet egress allowed by the subnet's NACL rules.

Implementation 1: PII-Aware Relay with Async Audit Logging

This is the core endpoint. Two details matter most: PII redaction runs before the upstream call so sensitive data never leaves your VPC, and the audit writer is fully decoupled so a slow disk cannot inflate user-facing latency.

# file: relay_station.py

Production AI API relay station with MLPS 2.0 Level 3 controls.


Tested on Python 3.11, FastAPI 0.110, openai 1.30.


import asyncio, hashlib, json, os, re, time
from datetime import datetime, timezone

import asyncpg
from fastapi import FastAPI, Header, HTTPException, Request
from openai import AsyncOpenAI

HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY  = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
PG_DSN             = os.getenv("PG_DSN", "postgres://audit:audit@db/audit")
ALLOWED_MODELS     = {"gpt-4.1", "claude-sonnet-4.5",
                      "gemini-2.5-flash", "deepseek-v3

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