I'm currently evaluating Software Composition Analysis (SCA) and vulnerability scanning solutions for a mature Java-based microservices environment, and I've narrowed the primary contenders to Synopsys Black Duck and JFrog Xray. Our deployment pattern heavily utilizes containerized applications on Kubernetes, with artifacts flowing through Artifactory as our binary repository manager. The primary objective is to shift security left by scanning binaries (JARs, WARs, and ultimately container images) post-build, rather than solely relying on source-level SCA during CI.
My initial analysis has surfaced several architectural and operational considerations where I'm seeking concrete, experience-driven feedback, particularly from teams running similar stacks.
**Key Requirements & Environment Context:**
* Primary artifact: Java applications (Spring Boot, Maven/Gradle).
* Critical need: Accurate deep scanning of nested dependencies within executable JARs/WARs, not just the top-level `pom.xml` or `build.gradle`.
* Pipeline integration: Scanning must be triggered automatically upon artifact promotion to a specific repository in Artifactory.
* Output: Findings must be actionable, with minimal false positives, and integrate seamlessly with our existing Grafana/Prometheus alerting and Jira ticketing systems.
* Operational model: We prefer an on-premises or self-hosted solution due to internal policy.
**Specific Points of Comparison I'm Investigating:**
* **Scanning Depth & Accuracy for Java Binaries:**
* Has anyone performed a direct comparison on the *same* complex Spring Boot JAR? I'm concerned about tools that only perform "bill of materials" reconciliation versus those that actually decompile and analyze the packaged dependencies. Initial tests show variance in vulnerability detection for transitive dependencies bundled into the final executable JAR.
* **Integration with JFrog Artifactory:**
* While Xray's integration is native, Black Duck offers its own integrations. In practice, how complex is the policy-driven automation for each? For example, automatically failing a build based on a Critical CVE found in a specific artifact path.
* **Performance & Resource Overhead:**
* For large binaries (300MB+), what has been the observed scan latency? Our artifact volume is significant, and scan time directly impacts developer feedback loops.
* **Vulnerability Database & Policy Management:**
* How configurable are the severity scores and policy rules? We need to tailor policies based on application context (e.g., a CVSS score for a vulnerability in a *dev* dependency may be treated differently than in a runtime dependency).
I am less interested in generic feature lists and more in operational experiences. For instance:
* Have you encountered challenges with custom or internally developed libraries?
* How is the alert fatigue managed? What's the process for suppressing or acknowledging false positives?
* What does the long-term maintenance look like for each platform (upgrades, database updates, etc.)?
Any insights, especially those backed by data or specific configuration examples, would be greatly valued. I will be conducting a proof of concept for both, but real-world war stories are invaluable.
—Chris
Data over dogma