Intelligent PII Detection & Anonymization
Context-aware, pluggable, and customizable data protection framework for text, images, and structured data. Democratizing de-identification technologies for privacy-compliant AI development.
▶️ Live Demo: PII Detection Pipeline
Real-time processing with Microsoft Presidio
Raw Input
Contains sensitive PII data
Analysis
NLP + Pattern Recognition
Protected Output
GDPR-compliant data
📥 Installation
💻 Real Implementation
from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine
# Initialize engines
analyzer = AnalyzerEngine()
anonymizer = AnonymizerEngine()
# Sample text with PII
text = "My name is John Doe and my phone is 212-555-5555"
# Analyze for PII
results = analyzer.analyze(text=text, language="en")
# Anonymize detected PII
anonymized = anonymizer.anonymize(text=text, analyzer_results=results)⚠️ Before (Raw Data)
"My name is John Doe and my phone is 212-555-5555"
✅ After (Anonymized)
"My name is [PERSON] and my phone is [PHONE_NUMBER]"
Enterprise-Grade Privacy Protection
The Perfect Stack: Presidio + Langfuse + GDPR
Pre-Processing
Presidio anonymizes training data before feeding into LLM pipelines
Monitoring
Langfuse tracks model performance with privacy-safe observability
Compliance
Automated audit trails ensure GDPR data minimization principles
❓ Discussion Question for LinkedIn
"When using observability tools like Langfuse to monitor LLM training pipelines, how do you balance detailed performance insights with GDPR's data minimization principle? Do you anonymize ALL training data upfront with Presidio, or use dynamic masking strategies?"
Ready to Build Privacy-Compliant AI?
Join thousands of developers using Microsoft Presidio to democratize de-identification technologies and build trustworthy AI applications.