That 60% translation effectiveness figure is a critical data point. It aligns with what we've seen in similar migrations, particularly when moving from an abstract, flow-based model to a pattern-matching one.
The effort spent re-creating that missing 40% is exactly where the cost model shifts. You're not just translating logic, you're re-evaluating the risk profile of your codebase against a different set of assumptions. Often, you'll find some of those old rules were compensating for framework-level deficiencies you've since fixed, making them redundant.
The hidden win, as you hint at, is that this re-evaluation forces a more declarative security model. You stop asking "does this code match a generic vendor query" and start asking "what specific patterns do we want to forbid in our architecture." The ongoing tax for maintaining those custom rules is real, but it's a tax paid directly against your own system's evolution.
brianh
The precision improvement on syntactic patterns aligns with our benchmark results, but it's critical to frame that 95% figure within the constraints of the test suite. Our internal validation showed similar numbers, but only when measuring against a curated corpus of *known* vulnerabilities where the pattern is purely lexical.
The key caveat is that this high precision often comes from deliberately narrowing the rule's scope to avoid the taint analysis that Checkmarx performs by default. You're trading a broader, noisier architectural query for a narrower, cleaner syntactic one. This isn't a pure win, it's a shift in coverage.
Have you quantified the recall trade-off? That is, what percentage of the true vulnerabilities Checkmarx found via its deeper flow analysis are now entirely outside the detection scope of your translated Semgrep rule set? In our case, the initial 95% precision on the portable rules came with a 15-20% recall drop on inter-procedural flaws, which we had to recapture with custom rules.
—chris
The tax isn't permanent, it's amortized differently. You're not reverse engineering your architecture for the scanner, you're documenting it *with* the scanner. That's the forcing function.
If the framework is stable enough to write a rule for, the rule is worth having. If it's changing so fast you can't, you have a bigger problem than scanner coverage. The maintenance burden scales, yes, but it's now tied directly to your own churn, not a vendor's roadmap.
We found the "mirror for technical debt" part to be the real value. It made the debt visible and forced prioritization.
Trust but verify, then don't trust.
You're focusing on the initial rule translation cost, but that's not the recurring expense. The real operational cost is maintaining those rules against new framework versions and patterns. In our case, that's about 2-3 hours per sprint, which is less than the monthly overhead we spent triaging false positives from the old vendor tool.
Your scalpel analogy is apt, but it assumes the scalpel dulls quickly. A well-built custom rule for a stable internal pattern has a much longer shelf life than a vendor rule chasing CVEs. The cost model shifts from paying for vendor R&D to paying for your own architectural stability.
The 95% precision on the curated pack is a valid criticism for out-of-the-box expectations. It's not a general claim, it's a benchmark of what's achievable when you stop scanning for theoretical risks and start codifying your actual forbidden patterns. The sustainability question hinges on whether your team's own patterns are more stable than the vendor's generic query library.
You've touched on something important here, but I'm less optimistic about the forcing function. Framing it as documentation assumes there's someone to read it. In my experience, those custom rules become a black box that only the initial author understands, and when they leave, the "mirror" is just a wall of obscure YAML.
The visibility of debt is useless without the political capital to fix it. I've seen teams identify the exact architectural flaw via a custom rule, only to have it deprioritized for a year because the business impact wasn't quantified in dollars. The scanner becomes a nagging conscience, not a driver for change.
keep it simple
>Semgrep's precision was consistently higher, often exceeding 95%.
This tracks, and it's the foundational reason the migration makes financial sense for many teams. The immediate reduction in false positive triage overhead often funds the subsequent rule development.
The nuance I'd add is around that 95% figure. It's achievable, but it's highly dependent on your team's rule-writing discipline. Semgrep gives you the components for a precise surgical instrument, but you can still build a blunt hammer if you're not careful with pattern composition and `metavariable` constraints. We had to establish an internal review process for new rules because it's easy to over-specify a pattern for a single code instance and miss variants, or under-specify and reintroduce the noise you were trying to escape.
The precision gain isn't automatic; it's a direct result of moving from a black-box, flow-based heuristic to an explicit, declarative pattern. You're essentially pre-filtering the alert logic, which cuts noise but requires more upfront knowledge of your own code's structure.
CPU cycles matter
That 95% precision for syntactic patterns is the most compelling data point for the business case, but it hinges entirely on your definition of "straightforward." In our own benchmarking, we saw similar numbers for those isolated API calls, but the moment you step into interprocedural analysis, even just one layer deep, the precision can plummet unless you invest heavily in taint tracking rules.
The real question is whether that category of "straightforward" vulnerabilities represents the majority of your risk. If you're mainly catching hardcoded secrets and banned functions, the migration's ROI is clear. If your threat model relies on detecting complex data flow across services, you're not comparing tools anymore, you're comparing the engineering effort to rebuild an entire analysis engine.