Hi everyone. First post here 👋 I've been tasked with improving our app security data pipeline. We used to rely on scheduled manual penetration tests for vulnerability data.
We recently integrated Checkmarx DAST into our CI/CD (Airflow DAG, Python-based). The *coverage* is fantastic—it scans everything automatically now.
But I'm finding the *depth* of findings... lacking? Compared to our old pentest reports.
* The DAST flags many low-hanging issues (e.g., missing security headers).
* It seems to miss more complex business logic flaws our testers found.
* Example: A flawed discount coupon workflow that allowed stacking wasn't caught.
Has anyone else experienced this gap? Is this a configuration issue, or just the nature of automated DAST vs. manual testing?
My current Checkmarx scan config is pretty basic:
```yaml
scan_type: full
policy: standard
```
Should I be combining this with another tool, or diving deeper into custom policies? Looking for any workflow advice.
Hey, I feel this. I'm a senior dev at a mid-sized e-commerce company, and I've been the one integrating security tooling into our Python/Go pipeline for the last two years. We run both Checkmarx DAST and SCA in production, alongside manual pentests twice a year.
**Core Breakdown:**
1. **Target Audience & Pricing:** Checkmarx is built for large enterprises. You don't pay per seat like SaaS tools; it's a six-figure annual commitment with a heavyweight on-prem scanner. The hidden cost is the internal ops overhead for upkeep. For us, that's about 15-20% of a senior infra engineer's time.
2. **Integration Effort:** Plugging the API into Airflow is the easy bit. The real lift was tuning. The default "standard" policy is noisy and shallow. To get anything close to depth, we had to build a custom policy mirroring our threat model, which took us two sprints.
3. **Where It Clearly Wins:** You nailed it: coverage and regression catching. It's fantastic for catching new instances of known, pattern-matchable flaws - like a new service launching without CSP headers, or a parameter suddenly open to SQLi. It scans everything, every night.
4. **The Honest Limitation:** It will **never** find novel business logic flaws. The scanner doesn't understand that "applying a discount" should decrement inventory, or that a "guest checkout" shouldn't be able to load another user's saved address. Our manual testers found a nearly identical coupon stacking flaw; the DAST just sees a valid HTTP request with a coupon code parameter.
**My Pick:**
For your specific gap, I'd recommend **keeping Checkmarx DAST for broad regression coverage, but augmenting it with scheduled, automated API security tests written by your dev/QA team.** Use a framework like OWASP ZAP as a library within your integration test suite to probe those complex workflows (e.g., "apply coupon A, then try to stack coupon B"). This gets you depth where it matters.
To make a cleaner call, tell us: 1) what's your team's appetite for writing/maintaining these more targeted security tests, and 2) are you blocked from running a lightweight, additional open-source scanner (like ZAP) in the pipeline for specific high-risk flows?
editor is my home
Spot on about the tuning lift. That custom policy phase is brutal, but it's the only way to get past surface-level stuff.
We found feeding it our API spec and past pentest reports as a "seed" helped a ton. The scanner started looking for specific weird parameter combos we'd seen exploited before. Still won't catch a brand new logic flaw, but it bridges some of the gap.
That ops overhead number is painfully real, too.
You've hit on the fundamental trade-off. DAST is automated, so it looks for known patterns. A human tester understands the *intent* of a discount workflow, then creatively violates it. The scanner just sees HTTP requests.
That gap you're seeing is absolutely normal, but you can narrow it. The standard policy is for baseline compliance. Start by building a custom policy that incorporates the specific vulnerability patterns from your past pentest reports. Feed the scanner your API specs, if you have them. It'll never fully replace a skilled tester for logic flaws, but a well-tuned policy can catch variations of previously discovered issues.
Think of it as a layered approach: DAST for continuous, broad coverage; scheduled manual pentests for depth. You've improved your coverage metric, now you need to improve the signal from the tool. The config tuning is the next, necessary pain point.
Always have a rollback plan.
Your experience with the coverage/depth trade-off is the expected baseline. The `policy: standard` setting is designed for compliance checkbox scanning - it's optimized for breadth, not depth.
You absolutely need to build a custom policy to start closing that gap. Treat your historical pentest reports as a training dataset. Manually translate each finding into a scanner rule. For your coupon stacking flaw, that means creating a rule sequence that probes for state manipulation across multiple API calls, which the standard policy won't model.
Consider this a hybrid approach: the DAST becomes a regression test for known flaw patterns, while you still schedule manual tests for novel logic exploration. The config lift is significant, but it's the only way to move beyond surface-level findings.
numbers don't lie
That 15-20% ops overhead for upkeep is a great, concrete number. It aligns with what I've seen. That time often goes into maintaining the custom policy as the app changes, right? It's not a one-time two-sprint cost, it's a recurring data pipeline to keep the threat model rules in sync with new features.
Your point about it being fantastic for regression on known flaws is spot on. That's the perfect use case. You're essentially building a automated, living regression suite from past pentest findings. The scanner won't invent new attacks, but it can tirelessly check that old ones don't creep back in.