Hi everyone 👋 I've been lurking here for a bit, trying to absorb all the AppSec knowledge. I'm not a security engineer by trade, but our dev team is growing and we know we need to get serious about tooling. The problem is... I'm completely overwhelmed.
There are so many categories: SAST, DAST, SCA, secret scanning, and they all seem to overlap. Then there are a million vendors, each claiming to be the best. We're a SaaS company with a modern stack (JavaScript/TypeScript frontend, Python/Go backend, GitLab CI, cloud-native). I've been tasked with evaluating options and making a recommendation.
My current approach is a giant spreadsheet, but it feels like I'm comparing apples to oranges. For example, do we start with a good SCA tool and add a linter? Or do we go for an all-in-one platform? How do you even prioritize when you have limited resources?
I'd love to hear from teams who have been through this. What was your practical process?
- How did you decide what to tackle first (like, secrets vs. dependencies vs. code analysis)?
- Did you prioritize easy wins in CI, or go straight for deeper, slower scans?
- Are there specific questions you asked vendors that revealed a lot?
Basically, how do you move from "we need AppSec" to "here's the tool we're implementing next quarter" without getting lost in the noise? Any frameworks or real-world lessons would be a lifesaver.
✌️ annie
Hey, good on you for tackling this. I was the "accidental security guy" on our team too. I'm a DevOps lead at a 70-person SaaS shop, similar stack - TypeScript/React frontend, Python/FastAPI and Go services, all on Kubernetes, CI in GitHub Actions. We run a combo of Snyk, Semgrep, and GitLab's built-in secret scanning in prod.
My spreadsheet had the same problem. Here's what mattered after we actually bought and ran things:
1. **SCA accuracy and noise** - This is your first big win. We tried a few free tiers; Snyk gave us fewer false positives on direct vs. transitive dependencies. For our 300-ish service repos, the initial scan flagged about 1200 vulns, but after auto-ignoring certain low-severity in dev deps, the actionable list was under 200. Another tool we trialed threw 3000+ alerts immediately, which would've buried us.
2. **CI pipeline slowdown** - This kills adoption. A SAST tool that adds 8+ minutes to a 12-minute build will get disabled by engineers. We measured: Semgrep with a ruleset for our languages takes 90-120 seconds on average for our monorepo. The all-in-one platform we tested added 6 minutes for the same scan. Ask vendors for cold vs. cached run times on a repo your size.
3. **Real pricing for mid-market** - The "contact sales" teams usually mean $25+/developer/month for the full suite. We found SCA alone often sits in the $4-something range, but adding SAST and DAST jumps it. Our final SCA+SAST combo runs about $9/dev/mo at our scale. Watch for commit-based pricing if you have many microservices - it can spike costs.
4. **Operational burden** - Where does the tool break? The all-in-one platform required a dedicated agent in our cluster for DAST, which was a compliance headache. A pure SaaS scanner had issues reaching our internal staging URLs. The winner for us ran mostly in CI, with a small, infrequent CLI scan for secrets in our Terraform code.
My pick: start with a dedicated SCA tool (we use Snyk) and add a lightweight, fast SAST linter (Semgrep) as a second phase. This gives you immediate dependency fixes and code smells without crushing velocity. If your compliance needs are low and you just need secrets scanning, GitLab or GitHub's native tools might be enough to start.
To make it cleaner, tell us: what's your average CI pipeline duration currently, and do you have any compliance requirements (SOC2, etc.) driving this?
it worked on my machine
The CI slowdown point is crucial, but you're only measuring the developer's wait time. Did you factor in the hidden infrastructure cost? Those extra minutes per build, across hundreds of services and multiple daily commits, translate directly into bigger, more expensive runner fleets or longer queue times. Vendors never quote you that total compute hour surcharge.
Also, be wary of "cold vs. cached run times" as a vendor metric. It's a rigged game. Their demo environment is a pristine, tiny codebase. The real pain is the incremental analysis on a large monorepo after a 10-line diff. Ask them for *that* benchmark instead.
trust but verify