Hi everyone! 👋 I’m pretty new to evaluating AI coding assistants and I’ve been tasked with looking into security workflows. I’ve heard a lot about Panther for security scanning, but I’m trying to understand how it works in a real, automated pipeline.
My main curiosity is around GitHub Actions. I see Panther has its own platform, but I’m nervous about setting up something complex that might break our existing CI. So I wanted to ask the community:
Has anyone actually tried integrating Panther with GitHub Actions for security scans? Specifically:
* Did you use an official action, or run the CLI in a custom step?
* What was the setup experience like? Was it straightforward or were there tricky configuration hurdles?
* How do you handle the results? Do you fail the build on critical findings, or just report them?
* Any "gotchas" or performance issues when running scans as part of a pull request check?
I’m excited to streamline our security reviews, but also a bit anxious about introducing a new tool into our workflow. Any experiences or lessons learned would be incredibly helpful!
Yes, we did this integration. Used the CLI in a custom step. The setup was straightforward, but the cost and performance are the real gotchas.
Running full Panther scans on every PR is expensive in compute time. It'll slow down your CI/CD. We only run it on the main branch nightly, with a subset of policies for PRs. Failing the build on critical findings is the only way to get developers to pay attention.
Biggest gotcha: the Panther platform cost itself spikes if you're scanning frequently with a large rule set. You're essentially paying for the compute twice - once for your GitHub Actions runner, and once for Panther's backend processing. Monitor your Panther bill closely after enabling.
cost per transaction is the only metric
Great question, and good call being cautious about CI stability. We also went the CLI route in a custom step.
I totally get the anxiety. To make it less scary, we started by running it as a non-blocking check in our PR template. It just comments with findings. After a month, we switched to failing on criticals. That gradual shift got buy-in.
One extra gotcha beyond cost: secrets handling. If your Panther policies scan for hardcoded secrets, make sure your step doesn't accidentally log the raw findings output. We masked the output initially to prevent leaking anything sensitive in the GitHub Actions logs.
How are you planning to manage the policy definitions? Keeping them in sync with Panther's cloud side can be its own headache.
git push and pray
The CLI route is definitely the way to go, no official action that I've seen.
But you're right to be nervous about breaking CI. My main gotcha was the sheer variability in scan times - sometimes 2 minutes, sometimes 15. Makes PR checks unpredictable. I ended up running a quick subset (just the "critical" rules) on PRs, and save the full policy suite for nightly runs on main. Saved our sanity.
And echoing the secrets warning - make sure your Panther API key is a repo secret, not an environment variable in the workflow file. Saw a team push that by accident once. Oops.
You nailed the exact feeling I had when we first looked at Panther for GitHub Actions. That anxiety about breaking CI is real, especially if your team is already cranking out PRs. We also went the CLI route in a custom step, and the setup was surprisingly straightforward. The real friction came later.
Everyone's right about cost and performance, but one thing I'd add from our marketing ops lens is data hygiene. When you set up those PR policies, you have to be so careful about what gets scanned. We accidentally started flagging our own campaign landing page templates as "suspicious HTML" because Panther's default policy set is tuned for production apps, not our content repos. Took a week to tune that noise out. 😅
How are you planning to handle policy management? Keeping your local rules in sync with Panther's cloud console became a second job for us until we wrote a little sync script. It's smooth now, but that was a hidden time sink.
Automate all the things.
We're using the CLI in a custom step too, and I agree with the gradual rollout approach others mentioned. The setup itself wasn't the tricky part for us.
Our main gotcha was policy drift between local test runs and the platform. We kept our policy definitions in a separate repo and used Terragrunt to sync them, treating Panther policies like infrastructure-as-code. Without that, we'd see "it passed locally but failed in Actions" because someone edited a rule in the UI and forgot to pull. Might be something to consider if you're already managing infra with code.
And on performance, the variance is real. We ended up caching the Panther CLI Docker image in our self-hosted runner, which shaved off a consistent minute or two from the "pull" step. Every bit helps when it's a PR check.