Zero-Trust Architecture for LLM Deployments

Zero-trust means "never trust, always verify" - every request gets verified, every access is logged, trust is earned per-request. Most LLM deployments violate every principle simultaneously.

The implicit trust problem

# What most applications do
response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_input}  # trusted implicitly
    ]
)
# No policy check. No PII filter. No audit log.
# user_input goes directly to OpenAI.

Four phases of zero-trust for LLMs

1. Identity and authentication. Every request must carry a verifiable identity - an API key scoped to a project with rate limits and permission sets. Anonymous requests are rejected before reaching the policy engine.

2. Policy evaluation. The request payload is evaluated against your policy rules. Rules match on message content, model name, estimated token count, or user-defined metadata. First BLOCK terminates the pipeline.

3. PII and content sanitization. After policy evaluation, PII filtering runs on the request body. Detected entities are redacted or blocked. The model never receives the original sensitive data.

4. Audit and non-repudiation. Every request - blocked or allowed - produces an immutable audit record. Records are cryptographically chained; any tampering breaks verification.

The proxy architecture

Your App
    │
    ▼
Autrace Gateway ──→ [Auth] ──→ [Policy] ──→ [PII Filter] ──→ [Audit]
    │                                                              │
    ▼                                                              ▼
LLM Provider                                               Audit Trail DB
(OpenAI / Anthropic / Gemini / ...)

Autrace is a true reverse proxy, not an SDK wrapper. BLOCK decisions are real - the request never reaches the provider.