Data Privacy - LangChain in Production

Learn how to protect sensitive data, implement privacy controls, and comply with data protection regulations in LangChain applications

🔒 Data Privacy Overview

Protecting user and business data is essential for trust and compliance. This guide covers privacy controls, PII detection, anonymization, and privacy-by-design patterns for LangChain systems.

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🛡️ Privacy Controls

• Mask or redact sensitive data in logs and outputs
• Use access controls for private data
• Enforce data minimization (collect only what is needed)

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🧑‍💻 PII Detection & Anonymization

• Use NLP models to detect PII (names, emails, addresses)
• Anonymize or pseudonymize data before storage or processing
• Audit data flows for privacy risks

import re

def mask_email(text):
    return re.sub(r"[\w\.-]+@[\w\.-]+", "[EMAIL REDACTED]", text)

sample = "Contact: alice@example.com"
print(mask_email(sample))

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🏛️ Privacy-by-Design Patterns

• Build privacy into system architecture from the start
• Use privacy impact assessments (PIA)
• Document privacy controls and decisions

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🧩 Example: FastAPI Data Redaction Middleware

from fastapi import FastAPI, Request
import re

app = FastAPI()

@app.middleware("http")
async def redact_middleware(request: Request, call_next):
    response = await call_next(request)
    if hasattr(response, "body"):
        response.body = re.sub(b"[\w\.-]+@[\w\.-]+", b"[EMAIL REDACTED]", response.body)
    return response

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🔗 Next Steps

• Governance Patterns
• Model Evaluation
• Continuous Improvement

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Key Data Privacy Takeaways:

• Mask and minimize sensitive data
• Detect and anonymize PII
• Build privacy into system design
• Audit and document privacy controls
• Continuously improve privacy posture