Day 66: Implement Log Redaction for Compliance
What We’re Building Today
Today we implement a production-grade log redaction system that automatically sanitizes sensitive data before storage and transmission:
Pattern-based redaction engine detecting PII, credentials, and regulated data types
Streaming redaction pipeline processing logs at ingestion with zero storage of raw sensitive data
Configurable redaction policies supporting GDPR, HIPAA, PCI-DSS compliance frameworks
Audit-preserving redaction maintaining investigative capability while protecting privacy
Why This Matters: The Compliance-Performance Paradox
When Uber expanded to Europe, they faced a critical challenge: their centralized logging infrastructure inadvertently stored millions of user phone numbers, email addresses, and location data across multiple regions. Under GDPR, each instance represented potential €20M fines. Their solution? Real-time redaction at ingestion, processing 2M events/second while guaranteeing zero sensitive data persistence.
The core tension in compliance-focused systems is this: regulations demand you never store sensitive data, yet debugging production incidents requires detailed logs. Traditional approaches—post-hoc scrubbing or field-level encryption—either fail audits (data existed, even temporarily) or make logs unusable for troubleshooting. Production systems need redaction that’s both immediate and intelligent, removing genuine risks while preserving investigative context.
At scale, this becomes a distributed systems problem. Netflix’s approach processes logs through three independent redaction layers before storage, accepting 15% performance overhead to guarantee compliance. Each layer uses different detection strategies (regex, ML-based entity recognition, schema validation) to catch what others miss. The architectural insight: compliance isn’t a feature you add—it’s a constraint that shapes your entire data flow.
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