Protecting Sensitive Data at Scale: Automated detection and remediation in a financial data lake

This article provides guidance for financial institutions on detecting and remediating sensitive data (PII and PCI DSS) at scale within AWS data lakes while maintaining compliance.

• Financial data lakes require automated protection across diverse data sources and formats
• Use AWS DMS and Amazon Macie to mask PII during batch database migrations
• Implement Amazon Comprehend and Kinesis Data Firehose for near-real-time streaming data redaction
• Leverage Amazon Macie for S3 bucket scanning using machine learning and pattern matching
• Apply AWS Glue managed PII detection transforms in ETL workflows with multiple processing options
• Use dynamic data masking in Amazon Redshift and Aurora PostgreSQL for query-time protection
• Implement S3 Object Lambda with Access Points to redact data before user retrieval
• Configure CloudWatch Logs data protection policies to mask sensitive operational logs
• Apply data lifecycle policies (DynamoDB TTL, S3 Lifecycle) to minimize sensitive data retention
• Enforce encryption at rest and fine-grained access controls across the data lake

AWS provides integrated managed services enabling automated sensitive data protection throughout the entire financial data lake lifecycle, from ingestion to consumption.