SAST vs DAST: Which Security Testing Do You Need

SAST (Static Application Security Testing) scans your source code or bytecode without running it, catching issues like SQL injection patterns and hardcoded secrets before you ship. DAST (Dynamic Application Security Testing) attacks a running instance of your app from the outside, the way a real attacker would, and finds problems like a misconfigured header or an auth bypass that only show up once the code is live. OWASP recommends both as standard practice in a mature DevSecOps pipeline.

What is SAST?

SAST tools read your codebase (source, bytecode, or sometimes compiled binaries) and flag patterns known to be dangerous, without ever executing the application. Think of it as a very thorough, security-focused linter. It walks control flow and data flow through your functions, tracing whether user input can reach a database query unescaped, whether a secret is sitting in a config file, or whether you called a deprecated crypto function.

Because SAST never runs the app, it can plug directly into your CI pipeline and scan every commit or pull request in seconds to minutes. That's the whole appeal: feedback lands before code merges, not after it deploys. Common findings include:

• SQL injection and command injection patterns
• Hardcoded API keys, passwords, and tokens
• Insecure use of cryptographic functions (weak ciphers, predictable random values)
• Unsafe deserialization
• Path traversal risks

Popular tools: Semgrep, CodeQL, Snyk Code, and SonarQube. Most integrate as a GitHub Action or GitLab CI job and annotate the pull request directly, so a developer sees the finding next to the line that caused it.

What is DAST?

DAST takes the opposite approach. It treats your application as a black box, with no access to source code, and probes it over HTTP the way an outside attacker would: sending malformed inputs, fuzzing parameters, trying default credentials, checking response headers, and walking authentication flows to see if they can be bypassed.

Because it's exercising the actual running system, DAST catches an entire category of bugs that static analysis structurally cannot see: a security header that's missing in the deployed config but present in a template, a session token that doesn't expire, an API endpoint that skips authorization in production even though the code path looks fine on paper, or a login flow with a logic flaw that only surfaces when you actually click through it.

Popular tools: OWASP ZAP, Burp Suite, and StackHawk. These typically run against a staging environment, either on a schedule or as a gate before a production deploy.

SAST vs DAST: the core trade-offs

The two approaches answer different questions, and each has structural limits the other doesn't share.

SAST is fast and shifts security left, catching problems while they're cheap to fix, but it has to guess. Without actually running the code, a static scanner can't always tell whether a flagged pattern is truly exploitable, which is why SAST tools are known for a higher false-positive rate. It also needs source code access, and it can't see anything that depends on runtime configuration, environment, or how services actually talk to each other in production.

DAST demonstrates a real exploit against a live target, so a finding is much more likely to be a genuine problem, which means a lower false-positive rate. It's also language and framework agnostic since it only cares about what goes over the wire, not what's underneath. The cost is speed and timing: it needs a deployed, running environment, so it runs later in the pipeline, and a scan can take much longer than a static pass. It also can't point you to the exact line of code responsible, only the endpoint and behavior it observed.

Comparison table: SAST vs DAST

	SAST	DAST
What it analyzes	Source code / bytecode	Running application (black-box)
When it runs	Early, on every commit or PR	Later, against a deployed/staging environment
False positive rate	Higher	Lower
Needs source code?	Yes	No
Finds runtime issues?	No	Yes
Speed	Fast (seconds to minutes)	Slower (minutes to hours)
Example tools	Semgrep, CodeQL, Snyk Code, SonarQube	OWASP ZAP, Burp Suite, StackHawk

Do you need both SAST and DAST?

Yes, and treating them as substitutes for each other is the mistake to avoid. SAST tells you about a risky pattern the moment you write it, before it's ever deployed anywhere. DAST tells you whether the deployed system actually behaves securely, headers, sessions, auth flows, and all the configuration drift that only exists once the app is live. Neither one covers the other's blind spot: SAST will never catch a misconfigured load balancer header, and DAST will never catch a hardcoded secret sitting unused in a branch that hasn't shipped yet.

There's also a middle ground worth a mention. IAST (Interactive Application Security Testing) instruments the app during test execution to observe real code paths as they run, and RASP (Runtime Application Self-Protection) does something similar in production, blocking attacks as they happen rather than just reporting them. Both aim to combine static precision with dynamic confidence, at the cost of runtime overhead and more setup.

For the bigger picture on where these fit alongside dependency and secrets scanning, see our application security best practices guide. If you're weighing SAST and DAST against SCA tooling specifically, our dependency and SCA scanning guide covers how vulnerable third-party packages fit into the same pipeline.

The call we'd make

Run SAST on every pull request. It's cheap, it's fast, and catching a hardcoded secret or an injection pattern before merge costs you almost nothing. Run DAST against staging before every production deploy, and on a recurring schedule against whatever's already live, since config drift doesn't wait for a deploy to happen. Neither tool replaces the other, and skipping either one leaves a gap the other was specifically built to close. If you can only stand up one this quarter, start with SAST for the speed of feedback, but put DAST on the roadmap immediately after. The teams that get burned are the ones that treat "we run a scanner" as a finished sentence instead of asking which kind, and for what.