Openrouter Data Privacy

OpenRouter Data Privacy

Overview

When sending data through OpenRouter to upstream LLM providers, you're responsible for ensuring prompts don't leak PII inappropriately. OpenRouter itself does not train on API data, but each upstream provider has its own data retention and training policies. This skill covers PII detection and redaction, placeholder substitution, provider selection for privacy, and consent tracking.

PII Detection and Redaction

import re
from dataclasses import dataclass
from typing import Optional

@dataclass
class PiiScanResult:
    clean_text: str
    findings: list[dict]
    has_pii: bool

PII_RULES = [
    ("email", r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'),
    ("phone", r'\b(?:\+1[-.\s]?)?\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}\b'),
    ("ssn", r'\b\d{3}-\d{2}-\d{4}\b'),
    ("credit_card", r'\b(?:\d{4}[- ]?){3}\d{4}\b'),
    ("api_key", r'\bsk-or-v1-[a-zA-Z0-9]+\b'),
    ("ip_address", r'\b(?:\d{1,3}\.){3}\d{1,3}\b'),
]

REPLACEMENTS = {
    "email": "[EMAIL]", "phone": "[PHONE]", "ssn": "[SSN]",
    "credit_card": "[CARD]", "api_key": "[API_KEY]", "ip_address": "[IP]",
}

def scan_and_redact(text: str) -> PiiScanResult:
    """Scan text for PII and return redacted version with findings."""
    findings = []
    clean = text
    for pii_type, pattern in PII_RULES:
        matches = re.findall(pattern, clean)
        for match in matches:
            findings.append({"type": pii_type, "value_prefix": match[:4] + "..."})
        clean = re.sub(pattern, REPLACEMENTS[pii_type], clean)

    return PiiScanResult(clean_text=clean, findings=findings, has_pii=len(findings) > 0)

Placeholder Substitution Pattern

import os, uuid
from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
    default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
)

class PrivacyProxy:
    """Replace PII with placeholders before API, restore after."""

    def __init__(self):
        self._map: dict[str, str] = {}

    def anonymize(self, text: str) -> str:
        """Replace PII with unique placeholders."""
        result = scan_and_redact(text)
        if not result.has_pii:
            return text

        # Use deterministic placeholders for consistent replacement
        anonymized = text
        for pii_type, pattern in PII_RULES:
            for match in re.finditer(pattern, anonymized):
                original = match.group()
                if original not in self._map:
                    placeholder = f"[{pii_type.upper()}_{len(self._map)}]"
                    self._map[placeholder] = original
                else:
                    placeholder = next(k for k, v in self._map.items() if v == original)
                anonymized = anonymized.replace(original, placeholder, 1)
        return anonymized

    def deanonymize(self, text: str) -> str:
        """Restore original values from placeholders."""
        result = text
        for placeholder, original in self._map.items():
            result = result.replace(placeholder, original)
        return result

# Usage
proxy = PrivacyProxy()
user_input = "Contact john@example.com or call 555-123-4567"
safe_input = proxy.anonymize(user_input)
# safe_input = "Contact [EMAIL_0] or call [PHONE_1]"

response = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",
    messages=[{"role": "user", "content": safe_input}],
    max_tokens=200,
)
# Restore PII in the response if model referenced it
result = proxy.deanonymize(response.choices[0].message.content)

Provider Selection for Privacy

# Force specific provider to control data handling
def privacy_aware_completion(messages, sensitivity="standard"):
    """Route to appropriate provider based on data sensitivity."""

    PRIVACY_CONFIG = {
        "public": {
            "model": "openai/gpt-4o-mini",
            "provider": None,  # Any provider OK
        },
        "standard": {
            "model": "anthropic/claude-3.5-sonnet",
            "provider": {"order": ["Anthropic"], "allow_fallbacks": False},
        },
        "sensitive": {
            "model": "anthropic/claude-3.5-sonnet",
            "provider": {"order": ["Anthropic"], "allow_fallbacks": False},
            # Add PII redaction as mandatory pre-processing
        },
    }

    config = PRIVACY_CONFIG.get(sensitivity, PRIVACY_CONFIG["standard"])
    extra = {}
    if config["provider"]:
        extra["extra_body"] = {"provider": config["provider"]}

    return client.chat.completions.create(
        model=config["model"],
        messages=messages,
        max_tokens=1024,
        **extra,
    )

Privacy Middleware

class PrivacyMiddleware:
    """Enforce privacy policies before every API call."""

    def __init__(self, block_on_pii: bool = False, auto_redact: bool = True):
        self.block_on_pii = block_on_pii
        self.auto_redact = auto_redact

    def process(self, messages: list[dict]) -> list[dict]:
        """Scan and optionally redact PII from all messages."""
        processed = []
        for msg in messages:
            content = msg.get("content", "")
            if isinstance(content, str):
                result = scan_and_redact(content)
                if result.has_pii:
                    if self.block_on_pii:
                        raise ValueError(f"PII detected: {[f['type'] for f in result.findings]}")
                    if self.auto_redact:
                        msg = {**msg, "content": result.clean_text}
            processed.append(msg)
        return processed

Error Handling

Enterprise Considerations

• OpenRouter does not train on API data; check each upstream provider's data use policy separately
• Use provider.order + allow_fallbacks: false to ensure data only flows to approved providers
• Implement PII redaction as middleware that runs on every request, not optional per-call
• For GDPR right-to-erasure: don't log raw prompts -- hash them (SHA-256)
• Use BYOK for sensitive workloads so data flows directly to the provider under your account
• Build a data classification system that auto-routes based on sensitivity level

References

• Examples | Errors
• Privacy Policy | Provider Routing