Having now overseen the deployment and operationalization of Semgrep across our entire AWS estate for half a year, I believe we have sufficient data to move beyond initial impressions and provide a substantive review. Our environment consists of approximately 200 developers, a mix of microservices and legacy monolithic applications, all deployed on AWS with infrastructure defined primarily in Terraform and CloudFormation. Our initial goal was to shift security and code quality left without introducing excessive friction or overwhelming the team with false positives.
The implementation followed a phased approach:
- **Phase 1:** Centralized rollout of the Semgrep CLI in CI/CD pipelines (GitHub Actions, Jenkins) for all repositories.
- **Phase 2:** Gradual onboarding of custom rules, beginning with AWS-specific security misconfigurations (e.g., S3 bucket policies, IAM role trust relationships) and company-specific code patterns.
- **Phase 3:** Integration of Semgrep Supply Chain (SSC) for dependency scanning and reachability analysis.
**Key Findings & Observations:**
* **Performance & Integration:** The tool is exceptionally fast, which was critical for developer adoption. Scans that previously took minutes with other tools now complete in seconds. The GitHub Actions integration is seamless, and the ability to fail builds only on high-confidence findings (via precision scoring) prevented early rebellion from development teams.
* **Rule Language & Customization:** The YAML-based rule syntax proved accessible to our senior security engineers, though not to the average developer. We successfully authored 47 custom rules targeting:
* Insecure use of internal AWS APIs (e.g., hardcoded regional endpoints).
* Deviations from internal logging and error-handling standards.
* Non-compliant Terraform modules (enforcing tagging standards, encryption settings).
The learning curve for more complex metavariable and taint-flow logic was steeper, requiring dedicated effort.
* **Effectiveness & Noise:** The out-of-the-box rulesets (especially `r2c-ci`, `aws`, and `terraform`) had a high false-positive rate initially—approximately 40% for our codebase. This necessitated a significant upfront investment in triaging and creating targeted rule exclusions via `paths:` and `pattern-either` constructs. After curation, our signal-to-noise ratio improved dramatically. The real value emerged from our custom rules, which now catch genuine issues pre-commit.
* **Pain Points & Considerations:**
* **Management Overhead:** While the CLI is free, managing a consistent rule set across 300+ repos requires discipline. We ended up maintaining a central rules repository and a lightweight orchestration layer to ensure uniformity.
* **SaaS vs. Self-Hosted:** We opted for the free tier of Semgrep Cloud Platform (SCP) for centralized findings management. The pull request comments and dashboard are valuable, but the free tier's limitations on private rule sharing and user management are felt at our scale. A paid tier is under evaluation.
* **Limited Language Depth:** For our core languages (Python, Java, JavaScript, Terraform), coverage is excellent. For niche or legacy languages in our estate (a small amount of Perl), support is naturally absent. This was an expected gap.
**Conclusion for Similar Organizations:**
Semgrep has proven to be a powerful and efficient engine for static analysis, particularly when you are willing to invest in tailoring it to your specific environment. Its greatest strength is speed and developer experience; its greatest demand is initial configuration and noise reduction. For a 200-user AWS shop, it is a strong recommendation, but with the caveat that you must allocate resources for the ongoing maintenance of your rule corpus and triage workflow. The return materializes not from the default rules, but from the internal standards you encode and enforce.
—Anna
Migrate slow, validate fast.