Hey everyone! 👋 I've been diving deep into Langfuse's on-premise offering lately because, let's be honest, in my world (healthtech marketing), sending customer data outside our virtual private cloud is a non-starter. We needed a robust way to use Langfuse for tracing and observability while guaranteeing that personally identifiable information never hit an external API, even by accident.
I figured a walkthrough of our PII redaction setup might be useful for others in regulated industries or anyone just super cautious about data privacy. The goal was to scrub data *before* the trace payload leaves our VPC bound for Langfuse's ingestion endpoint. We leaned heavily on the `input` and `output` processors in the Langfuse SDK.
Here’s the core of our approach:
* **We defined a redaction processor function.** This sits in our shared application config and uses a combination of regex patterns (for emails, phone numbers) and a library for detecting/scrubbing names. It recursively walks through the input/output objects.
* **We integrated it at the SDK initialization level.** This was key for ensuring no developer forgets to apply it on a per-call basis. We wrapped the Langfuse client creation to automatically attach the processors.
* **We also redact in our custom `score` and `event` calls.** Since these can contain free-text feedback or metadata, we run the same processor on the `value` and `comment` fields.
A simplified snippet of our processor logic looks something like this (conceptually—I won't paste the actual code since it's lengthy):
- **Pattern Library:** We maintain a list of regex patterns for known PII formats (e.g., `b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+.[A-Z|a-z]{2,}b`).
- **Contextual Redaction:** For things like names, we use a dedicated service that returns a redacted version. We also catch common placeholder keys in JSON objects (e.g., `"firstName"`, `"phone"`).
- **Fallback Strategy:** If something looks like potential PII but doesn't match a pattern, we hash it. This gives us consistency for debugging (same input -> same redacted string) without exposing the data.
The biggest **"aha"** moment was realizing we had to apply this not just to the main trace inputs/outputs, but also to any user-provided metadata or scores. A sales rep might type a customer name into a feedback field, for instance.
**A few pitfalls we encountered:**
* **Performance:** Processing large, nested outputs recursively added latency. We had to implement a depth limit and async processing where possible.
* **False Positives:** Our initial regex was too greedy and redacted parts of product codes that looked like phone numbers. Tuning the patterns is an ongoing process.
* **Logging:** We had to be careful that our own application logs, which sometimes dump the *unredacted* trace for debugging, were only written to our internal, secured log aggregation service.
The end result is that we get all the amazing visibility into our LLM calls and agent workflows through Langfuse's UI, but I can sleep at night knowing that if a trace contains `"Follow up with [email protected] about her recent MRI,"` what actually reaches Langfuse's servers is `"Follow up with [EMAIL_REDACTED] about her recent [MEDICAL_PROCEDURE_REDACTED]."`
It took some upfront work, but the data governance team is happy, and our developers don't have to think about it. Has anyone else implemented a similar pre-flight redaction layer? I'm curious if you used a different strategy or found other sneaky places PII can hide in traces.
This is such a critical pattern, especially in healthtech. The SDK initialization wrapper is smart - it turns a compliance requirement into a default, which is the only way it'll stick at scale.
I'm curious about one thing: how did you handle the redaction logic for more free-form text fields? Like, if a user's query or an LLM's output contains an unexpected format of PII that your regex might miss? We've seen teams use a secondary, more aggressive hash-everything pattern for certain high-risk trace categories as a safety net.