redact-proxy 0.1.4

The ngrok for PHI. HIPAA-compliant wrappers for LLM APIs.

Redact Proxy

The ngrok for PHI.

Drop-in replacements for OpenAI, Anthropic, and Gemini SDKs that automatically redact PHI before sending to the API. Helps keep PHI out of LLM calls.

You still use your existing OpenAI/Anthropic/Gemini API keys. Redact Proxy runs locally—no Redact API keys, no signup, no data leaves your machine except to your chosen LLM provider.

Installation

# Basic (OpenAI + fast detection)
pip install redact-proxy

# With additional providers
pip install redact-proxy[anthropic]
pip install redact-proxy[gemini]

# With enhanced detection
pip install redact-proxy[balanced]   # Adds Presidio NER
pip install redact-proxy[accurate]   # Adds transformer model

# Everything
pip install redact-proxy[all]

Limitations

⚠️ USA / HIPAA focus: Detection patterns are optimized for US healthcare data—US date formats, SSNs, US phone numbers, Medicare/Medicaid IDs, and US facility names. European identifiers (NHS numbers, EU formats, GDPR-specific PII) are not currently supported.

⚠️ Not a guarantee: This tool reduces risk but does not eliminate it. False negatives are possible. It does not provide BAAs, does not secure your application logs, and is not a substitute for a full compliance program.

Quick Start

OpenAI

# Before (PHI may be sent to LLM)
from openai import OpenAI

# After (PHI redacted locally before sending) - just change the import!
from redact_proxy import OpenAI

client = OpenAI(phi_detection="fast")

# Same API, PHI automatically redacted
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "Patient John Smith, DOB 01/15/1980, has diabetes"}
    ]
)
# OpenAI receives: "Patient [NAME], DOB [DATE], has diabetes"

Anthropic

from redact_proxy import Anthropic

client = Anthropic(phi_detection="fast")

response = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Patient John Smith has diabetes"}
    ]
)

Gemini

from redact_proxy import Gemini

client = Gemini(phi_detection="fast")

response = client.generate_content(
    "Patient John Smith has diabetes"
)

# Or use chat
chat = client.start_chat()
response = chat.send_message("Patient John Smith has diabetes")

How It Works

1. Detect — Scans your message for PHI (names, dates, SSNs, etc.) using pattern matching and optional NER
2. Replace — Substitutes PHI with placeholders like [NAME], [DATE], [SSN]
3. Forward — Sends the redacted request to your LLM provider using your existing API key

All processing happens locally. The LLM never sees the original PHI.

Detection Modes

Mode	Speed	Method	Use Case
fast	~1-5ms	Regex patterns	Real-time chat, most users
balanced	~20-50ms	Patterns + Presidio NER	Better name detection
accurate	~100-500ms	Patterns + Presidio + Transformer	Batch processing, high-risk

# Choose your mode
client = OpenAI(phi_detection="fast")      # Default - fastest
client = OpenAI(phi_detection="balanced")  # Better accuracy
client = OpenAI(phi_detection="accurate")  # Best accuracy

PHI Types Detected

• Names: Patient, provider, family member names
• Dates: DOB, visit dates, all date formats
• Ages: All age formats (65 y/o, 65-year-old, etc.)
• SSN: Social Security Numbers
• MRN: Medical Record Numbers
• Medicare/Medicaid IDs
• Phone/Fax numbers
• Email addresses
• Addresses: Street, city, state, ZIP
• URLs and IP addresses
• Facilities: 5,286 US hospitals + 12,130 skilled nursing facilities (from CMS)

Advanced Usage

Custom Placeholder

client = OpenAI(
    phi_detection="fast",
    redact_placeholder="<REDACTED:{phi_type}>"
)
# Output: "Patient <REDACTED:NAME> has diabetes"

Direct Detection

from redact_proxy import PHIDetector

detector = PHIDetector(mode="fast")

# Just detect
findings = detector.detect("Patient John Smith, DOB 01/15/1980")
for f in findings:
    print(f"{f.phi_type}: {f.text} (confidence: {f.confidence})")

# Detect and redact
redacted_text, findings = detector.redact("Patient John Smith, DOB 01/15/1980")
print(redacted_text)  # "Patient [NAME], DOB [DATE]"

Why Redact Proxy?

1. One-line migration: Just change your import
2. Zero infrastructure: Works entirely locally
3. Fast: Pattern-based detection in milliseconds
4. Configurable: Choose speed vs accuracy tradeoff
5. Comprehensive: Covers all 18 HIPAA Safe Harbor identifiers

Security Considerations

Redact Proxy redacts PHI from LLM requests, but other parts of your application can still leak PHI:

• Application logs: Your logging framework may capture request/response bodies
• Exception traces: Stack traces may include PHI from variables
• Analytics/APM tools: Request payloads sent to monitoring services
• LLM response caching: If you cache responses, ensure the cache is secure

Redacting the LLM call is one layer—review your full data flow.

License

MIT

Links

• Website: redact.health
• Product Page: redact.health/redact-proxy
• Issues: GitHub Issues