SAST vs DAST: what each one catches, and how to run both
SAST and DAST are often presented as competing purchases. They are not: they read different evidence. Static analysis reads the code you wrote; dynamic analysis reads the application you actually deployed. The interesting engineering question is not which one, but where each belongs in the pipeline.
What static analysis can see
SAST works on source, bytecode or an intermediate representation. Because it has the whole codebase, it can trace a value from an untrusted entry point to a dangerous sink across files and functions — long before anything is running.
- ▸Injection-prone dataflow: user input reaching SQL, shell, template or file APIs.
- ▸Hardcoded credentials, weak crypto choices and unsafe deserialization.
- ▸Insecure defaults in framework configuration and infrastructure-as-code.
- ▸Every branch, including code paths that no test currently exercises.
What dynamic analysis can see
DAST attacks the running application from the outside, with no source access. It sees the deployed reality: the reverse proxy, the auth middleware, the environment variables actually set, and the third-party services actually wired up.
- ▸Authentication and session handling flaws in the deployed configuration.
- ▸Access-control gaps such as horizontal privilege escalation between real accounts.
- ▸Server and header misconfiguration, TLS issues and exposed endpoints.
- ▸Behaviour of code you did not write, including runtime dependencies.
The blind spots each one has
SAST cannot know whether a route is reachable in production, whether a WAF blocks the payload, or whether the value in an environment variable is a real credential. DAST cannot reach code with no route to it, cannot explain why a response was vulnerable, and only covers the paths its crawler or test suite actually reached.
That asymmetry is why findings from both should be correlated rather than tracked in separate queues. A static finding confirmed by a dynamic probe is a verified issue; a static finding on an unreachable path is backlog.
Sequencing both in one pipeline
The ordering that survives contact with real teams puts fast feedback early and slow, environment-dependent checks later.
- ▸Pre-commit / pre-push — secret detection only. It is fast and the failure is unambiguous.
- ▸Pull request — SAST in diff mode, blocking on newly introduced critical and high findings.
- ▸Merge to main — full SAST baseline plus dependency and IaC scanning.
- ▸Staging deploy — authenticated DAST against a production-like environment, seeded with test accounts.
- ▸Production — continuous runtime monitoring, plus a scheduled DAST pass against a safe surface.
Where the AI layer helps most
AI adds the least value at detection and the most at correlation. Given a static dataflow finding, a dynamic probe result and a runtime signal for the same endpoint, a model can collapse them into one root cause, rank exploitability using surrounding code, and draft a fix in the project’s own idiom. That is the work that otherwise consumes a senior engineer’s week.
Frequently asked
Do I need both SAST and DAST?
For anything internet-facing, yes. SAST finds vulnerable code including unexercised paths; DAST finds deployment, auth and configuration flaws that only exist in a running environment. Neither substitutes for the other.
Which should I adopt first?
Start with secret detection and SAST in diff mode — they are the cheapest to run and give feedback inside the pull request. Add authenticated DAST against staging once you have a stable pre-production environment.
Is IAST or SCA a replacement?
No. SCA covers dependency risk and IAST instruments a running app for better precision, but neither reviews your own untested code paths the way SAST does, nor probes deployment configuration the way DAST does.
Put this into practice
Start scanning your own repositories free, or download the AI/LLM security checklist to audit what you already ship.