New: Pressure test your SOC 2 report against buyer requirements
Free check
Static application security testing (SAST) scans your source code for vulnerabilities before they ship. A good tool does four things well: it finds true vulnerabilities without burying you in false positives, it helps fix what it finds, it runs fast enough to gate every pull request, and it can prove its accuracy with a benchmark you can check. The traps to avoid are tools whose false positives train developers to ignore them, scanners that stop at a report instead of a fix, and pattern matchers that miss the authorization and logic flaws behind breaches.
Static application security testing (SAST) is a method that analyzes an application's source code, without running it, to find security vulnerabilities such as SQL injection, cross-site scripting, and hardcoded secrets. It runs early and often, on commits and pull requests, so issues surface while the code is fresh and cheap to fix. It is one layer of application security, alongside software composition analysis (SCA) for open-source dependencies and dynamic testing (DAST) for the running app.
SAST runs as a loop: scan, analyze, triage, fix, verify, and report. (Rendered as the lifecycle diagram; steps below and in the Lifecycle steps collection.)
If your team ships AI-generated code, you already need this. AI coding assistants write vulnerabilities into the code you ship, and they do it faster than a manual review can catch. Consider a dedicated SAST tool when any of these apply:
Detection is only the first third of the job. A modern SAST tool has to find vulnerabilities, help you decide which ones matter, and then help you close them. Score each tool on nine things, in this order. Accuracy comes first because a SAST tool that developers do not trust gets bypassed, and then nothing else matters.
Accuracy is a SAST tool's precision (the share of its findings that are true, exploitable bugs) and its recall (the share of genuine vulnerabilities it catches). It decides whether a tool holds up in daily use. On real codebases, more than 90 percent of SAST findings are noise; once about one finding in five is wrong, developers stop trusting the tool and route around it. Recall is the other half: on the OWASP Benchmark, a public suite of 2,740 Java test cases, even the strongest commercial engines score modestly (a Youden's Index around 0.30 to 0.39 in independent testing), so no tool catches everything, and a vendor's own false-positive number is not a neutral measure.
Determinism is a related quality signal that matters more as AI-assisted review becomes common. A scanner that returns different findings on the same code across runs cannot serve as a reliable gate. A vulnerability that shows up in two of three reviews is not a flaky test you can retry. It is uncontrolled coverage.
Key considerations:
Analysis depth is how the engine reasons about your code. Pattern matching flags a line that looks risky; data-flow (taint) analysis follows untrusted input from where it enters (the source) to where it is used (the sink), across functions and files. Pattern matching alone produces both misses and false positives: it flags an already-sanitized value, and it misses a genuine injection that passes through a helper method. That gap is the difference between a security tool and a linter.
Key considerations:
Coverage is the set of languages and frameworks a tool analyzes, and how deeply it analyzes each. It matters because a tool that does not parse your language leaves that code unscanned, and a high headline count can hide shallow support: a large language count may mean deep data-flow analysis for a few and simple pattern matching for the rest. Framework awareness (knowing how Spring, Django, or Rails route and sanitize) is what separates genuine coverage from a checkbox.
Infrastructure-as-code (IaC) and CI/CD pipeline files are another dimension of coverage worth checking. Misconfigurations in Terraform, Kubernetes manifests, Dockerfile, or GitHub Actions workflows are code vulnerabilities, and a SAST tool that covers them finds issues before they deploy.
Key considerations:
A finding with no path to a fix is backlog. The strongest tools produce the fix (a pull request) and then re-scan to confirm the issue is actually resolved, rather than trusting a closed ticket.
Key considerations:
Workflow fit is where findings appear and how fast they arrive. Findings that surface in the pull request, while the code is fresh, get fixed; findings in a separate dashboard get triaged later or never. Speed is part of this: a scan of a few minutes can run on every pull request, but past roughly fifteen minutes teams move it to nightly and lose the point of catching issues early.
Key considerations:
Triage is how the tool turns raw findings into a short, workable list, and how it remembers your decisions. Even an accurate tool overwhelms a small team without prioritization, and a tool that re-reports a finding you already dismissed gets abandoned. Suppression that persists across runs matters as much as detection accuracy.
Key considerations:
Your code is one source of risk, not the only one. Running a separate tool for each surface means several consoles and manual correlation, and lean teams pay the highest cost for that overhead. Many buyers are consolidating to a single platform that also covers open-source dependencies (SCA), secrets, containers, and the running app (DAST) in one correlated view, the model the industry calls application security posture management (ASPM).
Key considerations:
Code scanning has become a customer and audit expectation. The question is whether the evidence falls out of the work automatically or becomes a separate reporting project.
Key considerations:
Where the tool runs, how it handles your source code, and how its price scales are all worth checking before you buy. Some teams cannot send source code to a vendor's cloud. Pricing models scale very differently: a per-developer price and a per-line-of-code price diverge fast, and AI-generated code has increased how much code each scan covers.
Key considerations:
Score each tool across the whole loop, because a tool can be strong at finding and absent at fixing:
SAST analyzes your source code without running it, so it can point to the exact file and line. DAST tests the running application from the outside, so it catches issues that only appear at runtime. Teams use both, because each finds what the other misses.
SAST scans the code your team writes. Software composition analysis (SCA) scans the open-source dependencies you pull in, checking them against known vulnerabilities. Most application security programs run both.
They can. When too many findings are wrong, developers stop trusting the tool and bypass it, which is the most common reason a SAST program fails. The fix is to choose a tool with a low, provable false-positive rate and strong triage, and to scan only changed code so the queue stays short.
Partly. SAST is strong at pattern- and data-flow-based bugs like injection, and better tools also find some authorization and logic flaws such as IDOR. But SAST cannot see everything: configuration issues and many business-logic flaws still need dynamic testing or a human review.
On every commit or pull request, and as a fast check in CI, rather than on a schedule. The value is catching an issue while the code is fresh, so a scan that runs in minutes and comments on the pull request is worth more than a slower, deeper scan that runs nightly.
SAST helps. SOC 2 and ISO 27001 expect a documented process for finding and fixing code vulnerabilities, and a SAST tool that keeps a record of scans and remediations produces that evidence. Confirm with your auditor what they accept.
