PII Masking Patterns

Protect sensitive data in LLM observability pipelines with automated PII detection and redaction.

Overview

• Masking PII before logging prompts and responses

• Integrating with Langfuse tracing via mask callbacks

• Using Microsoft Presidio for enterprise-grade detection

• Implementing LLM Guard for input/output sanitization

• Pre-logging redaction with structlog/loguru

Quick Reference

Langfuse Mask Callback (Recommended)

import re from langfuse import Langfuse

def mask_pii(data, **kwargs): """Mask PII before sending to Langfuse.""" if isinstance(data, str): # Credit cards data = re.sub(r'\b(?:\d[ -]*?){13,19}\b', '[REDACTED_CC]', data) # Emails data = re.sub(r'\b[\w.-]+@[\w.-]+.\w+\b', '[REDACTED_EMAIL]', data) # Phone numbers data = re.sub(r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b', '[REDACTED_PHONE]', data) # SSN data = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[REDACTED_SSN]', data) return data

Initialize with masking

langfuse = Langfuse(mask=mask_pii)

Microsoft Presidio Pipeline

from presidio_analyzer import AnalyzerEngine from presidio_anonymizer import AnonymizerEngine

analyzer = AnalyzerEngine() anonymizer = AnonymizerEngine()

def anonymize_text(text: str, language: str = "en") -> str: """Detect and anonymize PII using Presidio.""" results = analyzer.analyze(text=text, language=language) anonymized = anonymizer.anonymize(text=text, analyzer_results=results) return anonymized.text

LLM Guard Sanitization

from llm_guard.input_scanners import Anonymize from llm_guard.output_scanners import Sensitive from llm_guard.vault import Vault

vault = Vault() # Stores original values for deanonymization

Input sanitization

input_scanner = Anonymize(vault, preamble="", language="en") sanitized_prompt, is_valid, risk_score = input_scanner.scan(prompt)

Output sanitization

output_scanner = Sensitive(entity_types=["PERSON", "EMAIL"], redact=True) sanitized_output, is_valid, risk_score = output_scanner.scan(prompt, response)

Key Decisions

Decision Recommendation

Detection engine Presidio (enterprise), regex (simple), LLM Guard (LLM pipelines)

Masking strategy Replace with type tokens [REDACTED_EMAIL] for debuggability

Performance Use async/batch processing for high-throughput

Langfuse integration Use mask= callback at client initialization

Reversibility Use LLM Guard Vault for deanonymization when needed

Anti-Patterns

❌ NEVER log raw PII

logger.info(f"User email: {user.email}") # PII leakage!

❌ NEVER send unmasked data to observability

langfuse.trace(input=raw_prompt) # May contain PII!

✅ ALWAYS mask before logging

logger.info(f"User email: {mask_email(user.email)}")

✅ ALWAYS use mask callback

langfuse = Langfuse(mask=mask_pii)

Detailed Documentation

Resource Description

references/presidio-integration.md Microsoft Presidio setup, custom recognizers, batch processing

references/langfuse-mask-callback.md Langfuse SDK mask implementation patterns

references/llm-guard-sanitization.md LLM Guard Anonymize/Deanonymize with Vault

references/logging-redaction.md structlog/loguru pre-logging patterns

checklists/pii-masking-setup-checklist.md Implementation checklist

Related Skills

• langfuse-observability

• Tracing with PII masking integration

• defense-in-depth

• Security layer including data protection

• advanced-guardrails

• LLM safety guardrails

• input-validation

• Input sanitization patterns

Capability Details

langfuse-masking

Keywords: langfuse mask, trace masking, observability pii, mask callback Solves:

• Mask PII in Langfuse traces

• Protect sensitive data in LLM observability

• GDPR compliance for LLM logging

presidio-detection

Keywords: presidio, pii detection, microsoft presidio, named entity, ner Solves:

• Detect PII using NLP models

• Custom entity recognizers

• Enterprise-grade PII detection

llm-guard-anonymization

Keywords: llm guard, anonymize, deanonymize, vault, sanitize Solves:

• Sanitize LLM inputs and outputs

• Reversible anonymization with Vault

• Input/output scanner pipeline

regex-masking

Keywords: regex, pattern matching, email mask, phone mask, ssn mask Solves:

• Simple pattern-based PII masking

• Lightweight masking without ML

• Custom pattern detection

logging-redaction

Keywords: structlog, loguru, logging, redact, pre-logging Solves:

• Redact PII before logging

• Structured logging with masking

• Log processor patterns