Hey folks, I’ve been deep in the weeds of Cloudflare One’s DLP suite lately, trying to see if it can actually solve a real-world headache for my team: preventing accidental source code leaks from our GitHub repositories.
We’re a product-led growth shop, and our engineers have access to a ton of repos, including some with legacy keys or sample configs that shouldn’t walk out the door. The classic scare is someone pushing to the wrong remote or a personal fork by mistake. Cloudflare’s Gateway with DLP seems like a logical firewall, but I’m curious about the practical, day-to-day experience.
Specifically, I’m experimenting with building policies that can identify code patterns, not just classic secrets like API keys. Think:
* Partial or complete source code matching for specific, sensitive projects.
* Detecting certain internal library or module names that shouldn’t be public.
* Blocking pushes that contain high-density patterns of our proprietary code format.
So, my questions for anyone who’s ventured down this path:
* **How are you structuring your policies?** Are you leaning more on pre-built profiles (like "Source Code") or crafting custom entries with regex for your unique fingerprints? I’ve been playing with custom word lists for internal project codenames.
* **What’s the performance/developer experience hit?** GitHub operations are so latency-sensitive. Have you noticed any meaningful delay in `git push` operations when Gateway is scanning? Any complaints from the engineering side?
* **False positives/negatives battle stories?** This is my biggest worry. Code is messy. How fine-tuned did your rules need to become to avoid blocking legitimate work? For example, how do you handle a README that contains example code snippets?
* **Integration scope:** Are you applying this to all HTTP/S traffic and hoping to catch web-based GitHub actions, or have you also looked at the Git protocol itself over SSH/HTTPS? The documentation seems a bit more focused on the web app side.
I’m currently running a small pilot with a volunteer dev cohort, tracking their error rates and sentiment. The ROI I’m looking for isn't just avoiding a breach, but also reducing the "oh crap" moment and the subsequent cleanup/rekeying labor. Would love to compare notes with anyone else running this experiment. The PLG part of my brain is itching to see if this is a feature that gets adopted willingly or one that’s just tolerated.
What’s working? What’s a total pitfall?
Try everything, keep what works.
Interesting angle. We tried something similar with our GitHub traffic a while back, but found the pre-built "Source Code" profile way too broad for a dev team - it flagged every single `git push` as a potential incident, which killed adoption fast.
We ended up layering: a base policy with a very tight custom regex for our core, unique internal module names (like `@ourcompany/secure-*`), and then a separate, higher-logging-only policy for the more generic patterns. The key was setting the custom policy to look for a **combination** of strings, not just one. That reduced false positives by a ton.
Have you looked into using fingerprinting? Cloudflare can do exact file matching, which is nuclear but perfect for that one super-sensitive legacy config file you mentioned.