Hey everyone, I just wrapped up the 30-day free trial of Semgrep Cloud for my team's data pipelines and wanted to share what I found. I'm pretty new to setting up security scanning, so this was a deep dive for me. Overall, I'm impressed but also a bit... overwhelmed by some of the findings!
We use mostly Python with Airflow, and our data lands in Snowflake. I connected our GitHub repo and let Semgrep do its thing. The good stuff first:
* The onboarding was super smooth. Connecting the repo and getting the first scan felt like magic.
* It caught some real issues we'd missed, like hardcoded credentials in old DAG files and some insecure deserialization.
* The PR comments are a game-changer. Seeing a rule violation directly in the pull request makes it so much easier to fix before merging.
But here's where I got a bit stuck. The volume of findings, especially from the "Security" ruleset, was huge. We got hundreds of alerts for things like `subprocess` calls and `assert` statements in our dev scripts. For data pipelines, some of these feel... noisy? Like, a script using `subprocess` to call `gsutil` in a controlled environment isn't the same risk as a web app.
I also tried writing a custom rule to catch something specific to our use case—like ensuring all Snowflake connections use a specific role parameter. The rule writing in YAML was okay, but I spent way too long debugging pattern matching. The documentation is good, but the learning curve is real.
My big question for others using it: **How do you manage the alert fatigue?** Did you start by disabling a bunch of default rules, or do you triage everything at the start? I'm worried about turning off something important.
The pricing seems fair for the value, but I need to get a handle on the workflow before I can confidently ask my manager to sign off. The "Code" (SAST) and "Supply Chain" (SCA) findings in one place is incredibly powerful. Just need to figure out how to make it sing for a data engineering context without drowning in alerts.
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That's a classic Semgrep onboarding experience. The initial flood from the bundled security rulesets can definitely feel overwhelming for data pipelines, where the context differs from web services.
You mentioned trying to write a custom rule, that's the exact right move. For those `subprocess` alerts, you can write a rule that checks for specific, trusted commands (like `gsutil`) and suppresses the finding. Or better yet, create a pipeline-specific ruleset that inherits from the main one but disables noisy rules like the one for `assert` statements in dev scripts. This lets you keep the high-signal findings while cutting the noise by 80%.
What was your experience like with the rule editor? I found their YAML structure pretty intuitive once you run through a few examples, especially for path-based exclusions.
yaml is my native language
That's a really good point about creating an inherited ruleset for the pipeline context. I haven't gone that far yet, as I'm still working through the initial results. The rule editor was where I spent the bulk of my trial time, actually.
I agree the YAML is intuitive for simple patterns, but I found the metavariable logic for more complex conditions a bit tricky. For instance, I tried writing a rule to flag our particular pattern of constructing Snowflake connection strings, and it kept matching more than I intended. The documentation helped, but I had to really think through the pattern operators.
You mentioned path-based exclusions. Did you find the pattern syntax for excluding entire directories reliable? I had a case where I excluded a `legacy_scripts/` folder, but it seemed like scans on new PRs still checked files there if they were modified.
Yeah, that initial noise wall is brutal. The bundled rules are built for the widest possible net, and data pipelines just don't fit that mold cleanly.
Your `subprocess` example is perfect. We hit the same thing. The real value kicked in for us when we stopped reviewing the generic findings and started building a pipeline-specific ruleset. We kept maybe 15% of the default rules and wrote a few dozen custom ones for our patterns. The noise dropped off a cliff.
Did you get a chance to try their rule board feature for triage? It's clunky but helped us tag all those false positives in bulk before we wrote the suppression rules.
Trust but verify.