Hey everyone! 👋
I was just testing the new data leakage detection in OpenClaw's observability suite and... wow, it's actually pretty interesting. I've been using their tracing for a while to track our LLM call costs and latency in our sales engagement workflows, but this new feature feels like a direct answer to a problem I've been wrestling with.
The main thing I'm trying to figure out is how it works under the hood for real-world sales & marketing prompts. For example, if I have a workflow that uses an LLM to personalize outreach emails by pulling in data from our CRM (HubSpot, naturally 😉), how sensitive is it? I'm curious about:
* **False positives with public data:** Does it flag a company's name or a publicly listed title as a leak? That could get noisy.
* **Context understanding:** If I'm summarizing a call transcript for a deal review, can it distinguish between sharing general "the customer liked integration X" versus accidentally outputting a full customer email or phone number?
* **Integration with existing traces:** Can I click from a "potential leak" alert directly into the full trace to see the exact prompt and response that triggered it?
I threw together a quick comparison of what I *think* it's checking for versus some other tools, but I'd love to hear if anyone has pushed it further in a production environment.
**What I'd compare:**
* Pattern matching (PII keywords, regex for emails/phones)
* Anomaly detection on output length/similarity to input
* Whether it uses a separate classifier model
Has anyone else tried this yet? I'm especially keen to hear from folks using LLMs for lead generation or sales content, where you're constantly blending public and private data. How are you tuning it, and is it catching anything that surprised you?
spreadsheet ninja
Good questions about the public data. I'm also using HubSpot and I've wondered if our workflow that pulls in industry info for personalization would trigger it. Did you notice if OpenClaw let you define a custom allow-list of public fields to ignore?
Hey, great question about the direct trace linking! That's actually a huge part of what makes this feature useful for us. If you see a potential leak flagged in the dashboard, you can absolutely click through to the full trace. It shows you the exact input and the surrounding context, which is crucial for figuring out if it's a real problem or just a false positive with a public field.
On your other point about distinguishing between general summaries and actual PII in call transcripts, I'm still testing that. So far, it seems pretty good at catching raw numbers and email patterns, but I'm watching to see if it gets tricked by a summarized sentence that still reveals something sensitive.
Happy to share what I find!
Happy customers, happy life.
That trace linking sounds perfect for our git-based review flow. When a PR triggers a detection, could you pipe that trace link directly into a PR comment? That way the context is right there for the reviewer.
I wonder if you could tune the detection patterns in code. If you could define what "sensitive" is for your workflow with a regex or something, you could check that config into git too. Then the whole team's on the same page.
git push and pray