Where Sensitive Data Hides in OpenTelemetry® Pipelines, and How to Find It

Juraci Paixão Kröhling of OllyGarden describes finding personal data, credentials, and secrets in real OpenTelemetry telemetry at banks, fintechs, health platforms, and IoT companies—in traces, metrics, and logs alike. Examples include a bank's trust store password, passed as a Java command-line flag, captured by the process resource detector into process.command_line and replicated onto every span; OAuth codes, access tokens, and JWTs in url.query and url.full, sometimes URL-encoded twice; bearer tokens in captured headers; Brazilian tax IDs in logs and metric attributes; clinician PII from an ORM logging every SQL statement with bound values; phone numbers as Prometheus labels; and database connection strings with embedded credentials. The post argues most leaks come from tools doing what they were configured to do—resource detectors, HTTP instrumentation defaults, wildcard header capture, ORM logging—rather than careless developers, and that SIEM tools miss them because they read text logs rather than structured OTLP data. It lists where to look (process.command_line, url.query, http headers, db.query.text, exception messages, serialized log bodies, metric attributes, AI prompts) and notes values matter more than attribute names. It then summarizes what GDPR, UK GDPR, the Australian Privacy Act, LGPD, CCPA, HIPAA, and PCI DSS 4.0 allow, including the client IP debate, and argues every personal attribute needs a justification for why it exists, who can see it, and how long it stays. Staging data is enough to find production leaks, and detection must tolerate false positives. 

https://ollygarden.com/resources/blog/where-sensitive-data-hides-in-opentelemetry-pipelines

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