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Evaluate and integrate third-party SAST and DAST tools

Implementation Effort: High – Identifying coverage gaps, evaluating candidate tools, and integrating and maintaining them in pipelines is an ongoing program rather than a one-time setup. User Impact: Low – Adding scanners to pipelines is an administrator and platform action; end users are not affected. Lifecycle Stage: Code

Overview​

Integrate third-party SAST tools where GitHub Code Security or GitHub Advanced Security for Azure DevOps does not fully cover your technology stack. This task follows the native SAST baseline established by implementing static analysis security testing (SAST): stand up CodeQL across your repositories first, confirm where it runs and what it covers, then turn to third-party tools only for verified coverage gaps. First identify those gaps, then select tools that address them without duplicating the capability you already get from CodeQL.

These tools cover a broad set of languages and vulnerability categories, but no single engine detects every class of defect across every technology stack. Organizations that rely on frameworks, languages, or build outputs with limited CodeQL coverage may need supplemental SAST such as binary analysis, framework-specific rules, or deeper taint analysis.

This task covers third-party SAST evaluation and integration, and the parallel evaluation of third-party DAST and IAST tools where native and built-in capabilities leave gaps. Treat DAST and IAST as complementary runtime testing controls rather than SAST alternatives: evaluate candidate third-party DAST tools here against the same criteria you use for SAST (coverage, false-positive rate, pipeline integration, and whether findings flow into existing developer workflows), then implement the selected DAST tooling in staging through the dedicated DAST-in-staging task, which owns the runtime DAST implementation and the staging-versus-production boundary.

When assessing third-party tooling, compare language coverage, false-positive rates, integration points with GitHub Actions or Azure Pipelines, and whether findings flow back into the same developer workflows that CodeQL already uses so developers do not need to context-switch between dashboards.

Organizations that skip this evaluation accept an implicit assumption that their default tooling catches everything, which is rarely true. Undetected vulnerabilities in production code give threat actors a persistent foothold that no amount of network-level controls can compensate for. Closing these coverage gaps supports Assume breach: layering complementary analyzers assumes any single engine will miss exploitable defects and adds defense in depth accordingly.

As you evaluate supplemental tooling, also track the emerging category of agentic, multi-model AI scanners. Microsoft's multi-model agentic scanning harness (codename MDASH) is the leading example and is covered as its own task — adopting agentic AI vulnerability discovery — so treat it there rather than as a third-party SAST product here. The separately announced integration between Microsoft Defender and GitHub Code Security is now generally available and provides an actionable way to enrich code findings with runtime context (such as internet exposure and data sensitivity) to inform prioritization.

Reference​