Hey everyone! I've been tasked with helping our team choose a SAST tool, and it's come down to Mend (formerly WhiteSource) and Fortify on Demand. I'm still pretty new to the whole AppSec side of data pipelines, so I'm hoping you can share your real-world experiences.
We're a Python and SQL shop, mostly building data transformation layers and APIs. I'm curious about the day-to-day stuff: How easy is it to integrate these into a CI/CD pipeline, say, with something like GitHub Actions or Airflow? I've heard Fortify can be quite complex to set up, but maybe it's more powerful? And Mend seems to be mentioned a lot for its ease of use.
Specifically, I'm wondering about:
* False positive rates in a codebase heavy with ETL logic.
* The learning curve for a team of data engineers who aren't security experts.
* How actionable the findings are. Do they give clear remediation guidance?
Our main goal is to catch issues early without slowing down deployments too much. Any horror stories or success tales with either tool in a similar environment would be super helpful!
-- rookie
rookie
I'm a security lead for a ~500 person fintech that runs a Python/Go/Postgres stack; I manage the SAST piece of our pipeline and we've used both Mend and Fortify on Demand in the last three years.
**Core Comparison**
1. **Integration Effort**: Mend's GitHub Action wrapper is a 10-line YAML file and it works, but it's basically a glorified API call. Fortify on Demand's pipeline integration required a dedicated Jenkins pipeline stage and about 80 lines of scripting to manage scan states and artifact upload. Their GitHub Action exists but feels bolted on.
2. **False Positive Rate for ETL/Data Logic**: With our Python data pipelines, Mend flagged a staggering number of "potentially unsafe deserialization" alerts on simple `json.loads()` calls in orchestration code. We tuned it down, but it still ran about a 40% FP rate. Fortify's rule packs for Python are more conservative; FPs were closer to 15-20%, but it missed a handful of subtle taint-flow issues in custom libraries.
3. **Remediation Guidance**: Mend's findings link to a generic vulnerability database entry, which is useless for context. Fortify provides a data flow trace from source to sink in their UI, showing the exact line path, which our junior engineers could actually follow.
4. **Cost and Scaling**: Mend's pricing was per-repository, which ballooned when we split our monorepo. At our scale, it landed around $7-9K/month. Fortify on Demand quotes started at ~$50K/year for a base pack, but that included a fixed number of scan hours; we hit overages twice during heavy dev periods, adding about 15%.
**My Pick**
I'd recommend Fortify on Demand if you have a dedicated AppSec person to manage the initial pipeline integration and tune rules. For a team of pure data engineers with zero security bandwidth, Mend gets you scanning faster but be prepared to drown in noise. Tell us your exact team size and if you have any compliance drivers (like SOC 2 or PCI-DSS), that changes the calculus.
Your point about Fortify's data flow trace being more actionable is critical. That feature alone can cut remediation time in half for complex vulnerabilities, especially in data pipelines where tainted data might flow through multiple transformation steps. I've found Mend's generic database links to be particularly unhelpful for custom internal libraries, where the vulnerability pattern is often tied to our specific implementation.
However, Fortify's superior guidance comes with a hidden cost: the trace visualization demands that developers context-switch into their security portal. This breaks the flow for engineers who live in the IDE. Mend's recent plugin does push findings directly into the editor, but as you noted, the findings themselves are less precise.
The integration complexity you described for Fortify isn't just a one-time setup cost. Maintaining those custom scripts across CI/CD platform updates becomes an ongoing burden, which often gets overlooked in total cost of ownership comparisons.
Measure twice, cut once.