After 18 months of evaluating and piloting, our organization has completed the full migration of our static application security testing (SAST) workload from Fortify SSC to Semgrep. This report details a 12-month post-deployment analysis, focusing on quantitative metrics, integration patterns, and operational overhead. Our scope encompasses 1,200+ microservices across five primary languages (Java, Go, Python, JavaScript, TypeScript).
## Executive Summary: Quantitative Outcomes
* **Scan Duration:** Average scan time reduced by **73%**. Fortify scans averaged 47 minutes per service; Semgrep averages 12.7 minutes under comparable conditions (equivalent rules, same build hosts).
* **False Positive Rate:** Our curated rule set (`security-audit` + custom) yields a **22% false positive rate**, down from approximately 35% with our tuned Fortify rule packs.
* **Operational Cost:** Annual licensing and infrastructure cost reduction of **~60%**, excluding FTE savings.
* **Developer Adoption:** PR comment resolution time (from SAST finding to developer action) decreased from 5.2 days to 1.8 days median.
## Deployment Architecture & Configuration
We run Semgrep in a hybrid model: CI jobs use the official `semgrep` CLI in `--json` mode, outputting results to a central service for deduplication and Jira ticketing. The key was moving from a monolithic scan model to a granular, pipeline-embedded one.
Our primary CI configuration block:
```yaml
# .semgrep.yml in repo root
rules:
- semgrep/rules/security-audit
- semgrep/rules/secrets
- /corp-rules/custom-jwt-check.yaml
- /corp-rules/aws-sdk-hardcoded-region.yaml
# corp-rules/custom-jwt-check.yaml
rules:
- id: hardcoded-jwt-secret
patterns:
- pattern: JWT.sign($PAYLOAD, "$SECRET", ...)
- pattern-inside: |
function $FUNC(...) {
...
}
message: "Hardcoded JWT secret in signing function. Use environment variable."
languages: [javascript, typescript]
severity: ERROR
fix: |
JWT.sign($PAYLOAD, process.env.JWT_SECRET, ...)
```
## Critical Success Factors & Pitfalls
**Successes:**
* **Custom Rule Velocity:** Development and deployment of a new custom rule (from concept to fleet-wide enforcement) now takes 2M LOC) timed out. Solved by implementing path-based scanning segmentation using `--include` flags and `paths:` directives in rule files.
* **Legacy Code Noise:** "Big Bang" enforcement on legacy codebases generated unmanageable ticket volume. We adopted a phased rollout: enforce new rules only on *new* PRs first, then gradually apply to modified files.
* **Lack of Data Flow:** Semgrep's taint tracking is capable but requires more explicit source/sink configuration than Fortify's out-of-the-box data flow rules. This increased initial setup time for taint analysis.
## Performance Benchmarks & Monitoring
We instrumented our scanning infrastructure with Prometheus metrics. Key observations:
* Memory consumption is consistently below 1GB per scan, even for large services, versus Fortify's frequent >4GB peaks.
* Scan time scales linearly with lines of code for our primary languages, with Python being the notable exception (~ 1.3x coefficient).
* The `--optimizations` flag provided a further 15% speed improvement after validation that it didn't suppress valid findings.
## Conclusion and Recommendations
For organizations with modern, polyglot codebases and a desire to shift security left, Semgrep presents a compelling, high-ROI alternative to traditional SAST platforms. Its primary advantage is not raw detection capability (Fortify's engine remains deep) but **operational efficiency** and **developer experience**.
Our recommendation is to pair Semgrep with a complementary tool for deep, inter-procedural data flow analysis on critical applications, while using Semgrep for the vast majority of high-velocity, pattern-based security and code quality checks. The migration path requires significant investment in custom rule creation to replicate institutional knowledge encoded in old Fortify rule packs.
-- elliot
Data first, decisions later.