Learn how to protect sensitive information in logs using Open Observe’s Sensitive Data Reduction feature. The video demonstrates how to detect and redact data such as passwords, tokens, emails, and credit card numbers through simple configuration without external tools.
Why sensitive data in logs is a security and compliance risk
Types of sensitive data commonly exposed in logs (PII, tokens, passwords, etc.)
How Open Observe’s Sensitive Data Reduction feature works
Difference between ingestion-time and query-time redaction
How to create and test sensitive data detection patterns
Methods to handle sensitive data (redact, hash, drop)
How to apply redaction rules to data streams
How to verify and explore redacted log data
This tutorial explains how sensitive data in logs and traces—such as personally identifiable information (PII), passwords, tokens, emails, and credit card numbers—can create serious security and compliance risks if left unprotected. It introduces Open Observe Enterprise’s Sensitive Data Reduction feature, which enables automatic detection and handling of such data.
The video walks through key concepts like ingestion-time versus query-time redaction, helping you understand when and how to apply each method. A practical demo shows how to define sensitive data patterns, test them, and apply them to data streams. It also covers different approaches to handling sensitive data, including redaction, hashing, and dropping fields entirely.
By the end, viewers will see how to implement a streamlined, built-in solution for securing logs without relying on external tools or complex workflows.
Simran specializes in DevOps, cloud-native technologies, and observability, with hands-on experience in Kubernetes, Docker, and AWS. Creates practical, accessible technical content and solutions that help teams simplify complex workflows and improve system reliability.
