SAST and DAST Tools 2026 — A Comparison

SAST and DAST get pitched against each other constantly, and it's the wrong frame. They answer different questions. SAST reads your source code without running it and asks "does this pattern look dangerous" — a string concatenated into a SQL query, a hardcoded key, a deserialization call on untrusted input. DAST runs your actual application and attacks it from the outside, the way a real request would hit it — throwing payloads at forms, headers, and parameters and watching what comes back. Neither replaces the other, and most of the teams I've seen struggle with security tooling are running exactly one of the two and assuming it covers the gap.

What SAST catches, and what it misses

Static analysis has gotten genuinely good at the categories it's built for: SQL injection, command injection, path traversal, hardcoded secrets, unsafe deserialization, and known-bad API usage (weak crypto primitives, disabled TLS verification, that kind of thing). Tools like Semgrep and GitHub's CodeQL work by matching code against rule patterns or building a data-flow graph and tracing whether tainted input reaches a dangerous sink. Snyk Code does similar data-flow analysis with its own rule set, layered on top of Snyk's dependency-scanning product. The strength of all three is that they run on every commit, in minutes, without a deployed environment — you get feedback on the same PR that introduced the bug.

What SAST is structurally bad at is anything that only exists at runtime: authentication and session bugs, business-logic flaws ("can user A access user B's invoice by changing an ID in the URL"), misconfigurations in the server or reverse proxy, and most of what's actually exploitable in a modern web app. A static analyzer reading your code has no idea your load balancer forwards the wrong header, or that your auth middleware is misapplied on one route out of forty. It also can't see anything happening in a dependency it doesn't have rules for, or in generated/dynamic code it can't trace cleanly.

What DAST catches, and what it misses

Dynamic analysis flips the vantage point: it treats your running app as a black box (or grey box, with some tools using an internal agent) and sends it malicious-looking traffic — malformed inputs, injection payloads, auth bypass attempts, forced browsing to hidden endpoints. OWASP ZAP and Burp Suite are the two names everyone reaches for, and they still dominate for good reason: ZAP is free, scriptable, and has a solid baseline scan mode built for CI; Burp is the tool most manual pentesters actually live in, with the Enterprise/CI edition doing scheduled or pipeline-triggered scans on top of the same scanning engine.

The tradeoff is that DAST needs a running, reachable target — which means a deployed environment, seeded test data, and often authentication set up so the scanner can reach logged-in pages, not just the login screen. It's also slower by nature: a thorough active scan can take anywhere from minutes to hours depending on how much of the app there is to crawl. And it will never point you at a line of code — it tells you "this endpoint returned a stack trace when I sent it a null byte," and you still have to go find where that happened.

Where each fits in CI/CD

SAST belongs early and often — on every pull request, ideally as a required check, because it's fast enough that nobody notices it running and cheap enough to run on every push. DAST belongs later in the pipeline, usually against a staging or ephemeral preview environment after deploy, because it needs something real to attack. Running a full active DAST scan against a PR that hasn't been deployed anywhere is not possible; running it against production without care is how you take production down or, worse, actually exploit your own users' data with test payloads.

A reasonable split I've settled into: SAST as a blocking PR check for high-confidence rules (secrets, injection, known-bad crypto), non-blocking for the noisier rule categories so it doesn't stall merges, and DAST as a scheduled or post-deploy job against staging, with results triaged by a human rather than gating the deploy automatically. Gating a deploy on a DAST scan sounds appealing until the first time it blocks a release over a false positive nobody can reproduce.

name: security-scans
on:
  pull_request:
  push:
    branches: [main]

jobs:
  sast:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run Semgrep
        uses: semgrep/semgrep-action@v1
        with:
          config: p/security-audit p/secrets

  dast:
    if: github.ref == 'refs/heads/main'
    needs: [deploy-staging]
    runs-on: ubuntu-latest
    steps:
      - name: ZAP baseline scan
        uses: zaproxy/action-baseline@v0.12.0
        with:
          target: 'https://staging.example.com'
          cmd_options: '-a'
          fail_action: false

Real tools and honest tradeoffs

Semgrep's biggest strength is that its rules are readable and writable by anyone on the team — a rule is a small YAML pattern, not a black box, so when it fires you can see exactly why and adjust it. CodeQL is more powerful for deep data-flow questions across a large codebase but has a steeper learning curve and is slower on first run; it earns its keep on large monorepos where the investment in custom queries pays off over time. Snyk Code's edge is that it lives in the same product as Snyk's dependency and container scanning, so you get one dashboard instead of three, which matters more for team adoption than for raw detection quality.

On the DAST side, ZAP's baseline scan is the honest choice for CI — it's passive-only by default (it doesn't actually attack anything, just crawls and checks headers/cookies/TLS config), which makes it safe to run against anything including production. Burp's strength is the manual testing workflow it enables — proxy, repeater, intruder — and its CI product is really "schedule the same scans a pentester would run," which is a different value proposition than "cheap automated gate." Neither tool replaces an actual penetration test against business logic; both are good at the mechanical stuff a human tester would get bored of doing by hand.

False positive fatigue is the real failure mode

The tool comparison matters less than what happens after the scan runs. SAST tools especially are prone to flagging patterns that are technically risky but contextually fine — a "hardcoded secret" that's actually a test fixture, a "SQL injection" in a query that's already parameterized three lines up in a way the analyzer didn't trace. The first few weeks after turning on a SAST tool are almost always a wave of noise, and if nobody triages it down, the team learns to ignore the whole channel. That's the actual security failure, not the missed vulnerability — a tool nobody reads is worse than no tool, because it creates the appearance of coverage.

IAST and SCA: the related pieces people forget

SAST and DAST get most of the attention, but two adjacent categories fill in real gaps. SCA (software composition analysis) — Snyk Open Source, Dependabot, OSV-Scanner — checks your dependency tree against known CVEs, which is a different problem from either SAST or DAST: your own code can be flawless and you can still ship a vulnerable version of a logging library. Given how much of a modern app is third-party code, SCA arguably catches more real-world incidents than SAST does. IAST (interactive application security testing) sits in between SAST and DAST — an agent instrumented inside the running app that watches real requests flow through and correlates them back to source lines, giving you DAST's runtime accuracy with SAST's code-level pointer. It's seen less adoption than either category, mostly because the instrumentation overhead and setup cost are real, but it's worth knowing it exists before assuming SAST-plus-DAST is the complete picture.

Wrapping up

If you only have budget and attention for one thing, start with SAST on PRs — it's cheap, fast, and catches a meaningful chunk of the classic bug classes before they merge. Add a DAST baseline scan against staging once you have somewhere stable to point it. But the tool choice is the easy part; the actual work is deciding who triages findings, how fast, and what gets to block a merge versus what gets filed and revisited. A pipeline with both SAST and DAST wired in and nobody reading the output is just a more expensive way of shipping the same bugs.