Hi everyone, I'm trying to set up our security scanning in our Jenkins CI/CD and I'm feeling a bit lost between two big options. Our team is leaning towards either Snyk or Veracode, but we're all data engineers, not security experts, and the docs are... a lot.
I'd love to hear from anyone who has practical experience integrating either one with Jenkins pipelines. Our main concerns are:
* **Ease of setup:** We have a mix of Python (Airflow DAGs, scripts) and SQL (dbt projects) in our repos. Which tool has clearer Jenkins plugin documentation or pipeline-as-code examples?
* **Pipeline speed:** We're scanning for vulnerabilities in dependencies and our own code. Does one tend to slow down the build significantly more than the other?
* **Actionable results:** We got burned by a tool that flooded us with false positives on internal libraries. How do their results look in the Jenkins console? Is it easy to triage?
I'm attaching a screenshot of our current (failed) attempt with a different scanner that just blocked every build 😅. We just need something that feels like a helpful guardrail, not a brick wall.
Any tips or gotchas you've run into would be massively appreciated! Especially if you've done this in a data pipeline context with lots of Python dependencies.
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I'm a platform engineer at a 400-person e-commerce company. We've been running security scanning in our Jenkins pipelines for about two years and currently use Snyk for our Python, Node, and Go microservices.
* **Jenkins plugin maturity**: Snyk's plugin felt more pipeline-native to me. Adding a `snykSecurity` step to our declarative pipelines took about 15 lines of configuration. Veracode's Jenkins integration at the time relied more on their command-line interface wrapped in shell steps, which added more boilerplate. For your Python and SQL repos, Snyk's documentation had specific examples for scanning Python dependencies and container images, which mapped directly to our use case.
* **Scan performance impact**: In my environment, a full Snyk scan on a moderate-sized Python service adds 45-90 seconds to the build time, depending on the number of dependencies. Veracode's static analysis (the SAST part) consistently added 3-5 minutes per pipeline run when we evaluated it. The speed difference came from Snyk doing primarily dependency scanning (SCA) in the pipeline and shifting deeper SAST to a separate, asynchronous step.
* **Results and noise**: Snyk's Jenkins console output lists vulnerabilities by severity, CVE, and a direct upgrade path, which our developers found immediately actionable. We had to configure a policy to ignore vulnerabilities in our internal libraries, which was a one-time setup. Veracode's findings were more detailed from a SAST perspective but required more security context to triage; we got feedback from devs that the findings felt "opaque" and slowed them down.
* **Pricing and model clarity**: Snyk's pricing is per developer seat, which at our scale was roughly $40-60 per developer per month. Veracode's pricing was project/application-based and less transparent; our quote was significantly higher and involved annual commitments. The operational cost for us was lower with Snyk because we didn't need a dedicated security engineer to manage the pipeline integration.
Given your team's background in data engineering and need for clear, actionable results without massive pipeline delays, I'd recommend Snyk for your Jenkins setup. If your compliance requirements mandate deep, traditional SAST on every commit regardless of speed, then look at Veracode. To make a clean call, tell us your budget per developer per year and whether you have a dedicated security person to manage the tool's output.
Data is sacred.
That speed difference is huge for us. We're trying to get the team to buy into security scanning, and adding minutes is a hard sell.
You mentioned >the speed difference came from Snyk doing primarily dependency scanning (SCA) in the pipeline and shifting deeper SAST to a separate, asynchronous step. That makes sense. Does that mean you get the pipeline results without the full SAST findings right away? How do you handle that? Does it just report later?
Both of them will drown you in false positives on custom SQL and Python scripts. That's their business model. You'll spend more time whitelisting internal Airflow operators than fixing actual vulnerabilities.
The screenshot is your future. Every internal dbt macro gets flagged as a potential injection, every import gets flagged. Their Jenkins plugins are just fancy wrappers for the same noisy engines.
You're data engineers, not a security team. Don't pick a tool that requires one to manage it.
Just saying.
Totally get the feeling of being swamped by docs! I just got Snyk working in our Jenkins for some basic Python scripts last week, and the plugin setup was surprisingly smooth. The tutorial for Python was a lifesaver.
But I'm already nervous about what user536 said regarding false positives on custom SQL and scripts. I haven't pushed it to our dbt projects yet. Did you find the Snyk plugin results in the Jenkins console manageable for triage, or does it still feel like noise?
Thanks, this is the concrete timing data I was looking for. The 45-90 second vs 3-5 minute difference for the pipeline step is huge.
You said Snyk shifts deeper SAST to a separate step. How does that work with developer workflow? If a critical SAST issue is found later, does it block a merge retroactively, or is it just a report?
Totally get the feeling about docs being overwhelming. I'm new to this too, and the setup guides can be a lot.
For your point about actionable results, I'm curious about the same thing. If a tool flags a custom dbt macro as a potential issue, how do you even start to tell if it's a real problem or just noise? The Jenkins console output from these plugins - is it just a huge list, or can you actually see the context?