SAST scans source code in CI, DAST probes deployed applications from the outside, and SCA maps your dependency tree to known CVEs. On paper, that covers the full attack surface. In practice, each method has blind spots the others cannot see: static analysis misses runtime behavior, DAST misses unexposed code paths, and SCA misses logic flaws in your own code. Adding IAST or RASP helps fill some of those gaps, but comes at the cost of runtime overhead and deployment complexity. This guide covers what SAST and DAST each catch, where SCA fits in, how tools like SonarQube compare across approaches, and where AI-native scanning closes the coverage gaps left by rule-based engines.
TLDR:
- SAST scans source code before it runs, DAST probes deployed apps from outside
- SAST catches vulnerabilities early in CI but produces 30-70% false positives
- DAST confirms real runtime flaws but misses source-level bugs and logic errors
- SCA checks dependencies for known CVEs, filling a gap neither SAST nor DAST covers
- ZeroPath uses AI to validate findings before surfacing them, cutting false positives by up to 75%
What is SAST (static application security testing)
SAST analyzes source code, bytecode, or binaries without executing the application. Scanners parse code into an abstract syntax tree or control flow graph, then trace data from sources to sinks, looking for patterns that match known vulnerability classes, such as SQL injection, XSS, path traversal, and similar issues.
Because SAST runs directly against code, it fits naturally into CI/CD pipelines and can flag vulnerabilities before a single line ships to production.
What is DAST (dynamic application security testing)
DAST tests a running application by sending real HTTP requests, fuzzing inputs, and observing how the app responds. Where SAST reads source code, DAST interacts with the deployed app from the outside, the way an attacker would.
A DAST scan requires no access to source code. The scanner probes live endpoints, injects payloads, and flags behaviors like unexpected redirects, authentication bypasses, or data leakage in responses.
DAST strengths
- Injection flaws that only surface at runtime, such as SQL injection and XSS, reflected through a live response
- Authentication and session management weaknesses are visible only through actual HTTP flows
- Server misconfigurations and exposed endpoints that static analysis cannot see
Where DAST falls short
- It finds nothing until the app is deployed, making it a late-stage gate
- Coverage depends on how thoroughly the scanner can crawl the app, so unauthenticated or poorly documented endpoints often go untested
- Results carry no line-of-code context, so developers must trace findings back to the source manually
Key differences between SAST and DAST
Timing is the defining split: SAST scans source code before the application runs, while DAST probes a live, deployed application from the outside.
Here are the key technical differences that matter in practice:
- SAST gives developers line-level feedback early, when a fix costs minutes. DAST catches what only appears at runtime, but by then the code is already in staging or production.
- SAST can analyze the full codebase and trace data flows across files and functions. DAST sees only what the application exposes through its HTTP interface, so anything not reachable via a request goes unexamined.
- SAST produces more false positives because it lacks execution context. DAST confirms real behavior, so its findings are nearly always exploitable, but it misses a wide class of source-level flaws entirely.
- SAST requires access to source code. DAST works against any running app, regardless of language or framework, making it one of the few options for testing third-party software.
Dimension | SAST | DAST |
|---|---|---|
When it runs | Pre-build or CI | Staging or production |
What it needs | Source code | Running application |
False positive rate | Higher | Lower |
Fix cost | Low (early) | Higher (late) |
Covers logic flaws | Sometimes | Rarely |
Covers runtime issues | No | Yes |
Neither approach covers the other's blind spots, which is why most security programs run both.
What SAST detects vs what DAST detects
SAST finds vulnerabilities written directly into the code:
- SQL injection and command injection patterns in query construction
- Hardcoded credentials and secrets embedded in source files
- Buffer overflows and memory safety errors in low-level code
- Insecure cryptography choices and weak hashing algorithms
- Path traversal and file inclusion flaws in I/O handling
DAST finds what only appears under real HTTP traffic, including vulnerabilities in API security that static analysis cannot detect:
- Authentication bypasses triggered by specific request sequences
- Session fixation and token reuse are visible only in live flows
- Server misconfigurations that no static scanner can observe
- Business logic flaws requiring multi-step interaction to surface
The blind spots run in both directions. SAST won't catch a session cookie missing the Secure flag, because that only manifests as an actual HTTP response header. DAST won't find a hardcoded API key buried in a utility function that it never reaches. Each tool is blind to the vulnerabilities the other sees most clearly.
When to use SAST vs when to use DAST
SAST fits best early in development, before code ever runs. When a developer opens a pull request, SAST can scan the diff immediately and flag injection flaws, hardcoded secrets, or insecure API patterns without spinning up an environment. That immediacy is the point: catching a vulnerability at commit time costs a fraction of what it costs post-deployment.
DAST belongs later, against a running application. It catches what static analysis structurally cannot: authentication bypass through session token reuse, server-side request forgery triggered by live HTTP calls, or input validation failures that only surface when the app processes real requests.
A few heuristics that hold up in practice:
- Use SAST when you want feedback within the developer workflow before any deployment. It works without a runtime infrastructure and fits naturally into CI gates.
- Use DAST to validate that deployed controls behave as intended, beyond whether the code looks correct on paper.
- Use both when regulatory requirements demand defense-in-depth evidence, since SAST and DAST failures tend to be non-overlapping in practice.
Neither tool makes the other redundant. SAST finds issues your DAST scanner will never detect because the code path isn't exposed externally. DAST produces findings that your SAST scanner will never flag because the vulnerability exists only in the interaction between components at runtime.
SAST and DAST false positives: what to expect
SAST typically generates more false positives than DAST. Static analysis flags every code path that appears suspicious, regardless of whether it is reachable in production. According to industry studies and user surveys, false-positive rates for rule-based SAST tools commonly range from 30% to 70%, meaning engineers spend considerable time triaging noise instead of fixing real vulnerabilities.
DAST false positives are lower but carry a different cost: false negatives. Because DAST only sees what it can reach within a running app, vulnerabilities buried in untested code paths go undetected.
AI-driven SAST cuts this gap by validating whether a flagged code path is actually reachable and exploitable before surfacing the finding. For teams evaluating AI SAST versus traditional approaches, the reduction in triage overhead is measurable.
Software composition analysis (SCA): the third pillar
SCA tools analyze your third-party and open-source dependencies for known vulnerabilities, license violations, and outdated packages. Where SAST reads your code, SCA reads your dependency tree.
The core output is a mapping of components to CVEs, typically scored by CVSS-based severity. SCA tools also flag license risk, which matters to legal and compliance teams reviewing OSS usage.
Key capabilities to expect from SCA tooling:
- Dependency graph generation, so you can see transitive dependencies pulling in vulnerable packages, and transitive ones beyond direct ones.
- Reachability analysis in more advanced tools, filtering CVEs to only those your code actually calls.
- SBOM (Software Bill of Materials) export is now required under several regulatory frameworks.
SCA sits alongside SAST rather than replacing it. A dependency can be patched; a logic flaw in your own code cannot.
IAST and RASP: runtime security methods
IAST sits between static analysis and runtime testing by instrumenting the application at runtime, typically through agents or sensors embedded in the code. Unlike DAST, which probes from outside, IAST observes actual execution paths as tests run, giving it visibility into both the request and the internal code flow that produced a response.
In practice, an IAST agent hooks into the application's runtime, the JVM, the .NET CLR, or a Python/Node interpreter, and monitors taint propagation as requests flow through. When user-controlled input reaches a dangerous sink without proper sanitization, the agent flags it with file and line context. Because the finding comes from a live execution trace, false positives are low; the vulnerability actually ran. The trade-off is overhead: instrumented applications can experience 5–15% latency increases, limiting IAST to QA and staging environments rather than production.
Coverage is the other constraint. IAST only flags code that actually executed during the test run, so any path not covered by your test suite is invisible. Pair it with thorough integration tests, and it punches above its weight; run it against a thin smoke suite, and you'll miss most of the attack surface.
RASP takes a similar runtime position but acts as a control mechanism, not a passive observer.
The practical cost is real, though.
Neither fits neatly into a CI/CD pipeline the way SAST or SCA does. IAST requires a running, instrumented app with active test coverage. RASP is a production defense, not a pre-ship gate. Both add runtime depth that static tools cannot reach, but they come with deployment complexity that SAST and SCA avoid entirely.
Popular SAST tools and how they work
Several SAST tools dominate the market, each with distinct architectural approaches worth knowing before you compare options.
Open source and free options
- SonarQube (Community Edition) is the most widely deployed open-source SAST tool, supporting 30+ languages and using a rule-based engine. It flags code smells alongside security issues, which can blur triage priority.
- Semgrep OSS lets teams write custom rules in YAML against a syntax tree, making it fast and hackable but rule-quality-dependent.
- Bandit specifically covers Python, scanning for common issues such as hardcoded credentials and unsafe deserialization.
- CodeQL (free for open source) runs dataflow queries against a compiled code database, which means it requires a successful build before scanning.
Commercial tools
- Checkmarx and Veracode are legacy enterprise SAST tools built on pattern-matching engines, often cited for high false-positive rates and slow scan times in larger codebases.
- Snyk Code (acquired from DeepCode) layers AI suggestions on top of a rule-based core, blending SCA and SAST in one interface.
- GitHub Advanced Security bundles CodeQL into pull request workflows, convenient for GitHub-native teams but limited by CodeQL's build requirement.
Popular DAST tools and how they work
DAST tools test running applications by sending real HTTP requests and observing how the app responds. Unlike static analysis, they require a live target and have no visibility into source code.
The most widely used options include:
- OWASP ZAP (Zed Attack Proxy): A free, open-source scanner maintained by OWASP. It supports both automated scanning and manual interception, making it common in CI/CD pipelines and internal security programs.
- Burp Suite: The de facto standard for manual web application testing. The Pro edition adds an active scanner; the Enterprise edition targets automated pipeline use. Widely used by penetration testers.
- Nikto: A lightweight, open-source command-line scanner focused on server misconfigurations, outdated software, and common vulnerabilities. Fast to run, limited in depth.
- Nuclei: A template-driven scanner from ProjectDiscovery. Security teams write or pull community templates to check for specific CVEs and vulnerability patterns at scale.
Each tool sends crafted inputs, observes HTTP responses, and flags anomalies such as unexpected redirects, error messages that leak stack traces, or authentication bypasses. Coverage depends entirely on how much of the application the tool can reach during the scan window.
Combining SAST, DAST, and SCA for complete coverage
No organization runs on a single testing method. The real question is how to layer SAST, DAST, and SCA so that each tool covers the gaps left by the others.
A practical starting point is to run SAST and SCA in CI on every pull request, catching vulnerabilities and risky dependencies before code merges. DAST then runs against a staging environment post-deploy, catching runtime and configuration issues that static analysis cannot see. Together, they cover three distinct attack surfaces: the code itself, its dependencies, and its live behavior under HTTP traffic.
Where each method fits in the pipeline
- SAST belongs at the earliest stage, scanning source code in the developer's editor or in CI before a PR merges. Fast feedback here is the point.
- SCA runs alongside SAST, checking whether any dependencies pulled into the build contain known CVEs or license conflicts.
- DAST runs later, against a deployed instance, probing endpoints the way an attacker would.
The coverage gaps that remain
Even with all three running, gaps exist. SAST and SCA miss runtime-only issues, such as authentication flaws and misconfigured headers. DAST misses source-level logic bugs and cannot see third-party library internals. Business logic vulnerabilities frequently fall through all three because no tool understands intent without deeper code reasoning.
Layer | What it catches | What it misses |
|---|---|---|
SAST | Source-level bugs, insecure patterns | Runtime behavior, config issues |
DAST | Live endpoint flaws, auth issues | Source logic, dependency CVEs |
SCA | Known CVEs in dependencies | Custom code vulnerabilities |
The gap in business logic coverage is where AI-native approaches, such as ZeroPath's, go beyond pattern matching by tracing data flow end-to-end to catch what rule-based engines miss.
How ZeroPath changes the SAST equation
ZeroPath goes beyond pattern matching by using AI to trace data flow from source to sink, catching business logic flaws that rule-based engines miss entirely. Every finding gets AI validation before it surfaces, which cuts false positives by up to 75% based on ZeroPath's internal data, so your team spends time on real bugs instead of noise. For a deeper comparison of AI SAST tools, see how different platforms stack up against traditional approaches.
It covers SAST, SCA, and secrets detection in a single scan, with results delivered in under a minute and fixes delivered directly in the PR.
Final Thoughts on Application Security Testing Methods
SAST, DAST, and SCA solve different problems, so combining them is the baseline for coverage. SAST runs in CI and provides line-level feedback on source code; DAST tests the deployed app and catches issues that only surface at runtime; SCA maps your dependencies to known CVEs. The overlap is minimal, which is why you can't skip any of them without leaving entire vulnerability classes undetected. See ZeroPath in action: it combines SAST, SCA, and secrets into a single scan, with AI validation and fixes delivered in your PR.
FAQ
SAST vs DAST: which one should I run first?
Run SAST first, in CI on every pull request. It catches source-level vulnerabilities before code ever runs, when fixes cost minutes instead of hours. DAST belongs later, against a deployed staging environment, where it can catch runtime and configuration issues that static analysis cannot see.
Can SAST tools detect business logic vulnerabilities?
Traditional pattern-matching SAST tools miss business logic flaws entirely because they only flag code that matches known vulnerability signatures. AI-native SAST, like ZeroPath, traces data flow end-to-end and understands code intent, catching authentication bypasses, authorization failures, and workflow logic errors that rule-based engines miss.
What is the difference between SAST and SCA?
SAST analyzes your source code for vulnerabilities such as SQL injection and hardcoded secrets. SCA analyzes your third-party dependencies for known CVEs and license violations. SAST reads the code you wrote; SCA reads the dependency tree and maps components to published vulnerabilities.
Is SonarQube SAST or DAST?
SonarQube is a SAST tool—it analyzes source code without executing the application. It uses a rule-based engine to flag security issues and code smells across 30+ languages, but it does not perform dynamic testing against a running application.
SAST vs DAST vs SCA: Do I need all three?
Yes, for complete coverage. SAST finds source-level bugs before deployment, DAST catches runtime and configuration issues in live apps, and SCA flags vulnerable dependencies. Each tool covers gaps left by the others. SAST misses server misconfigurations, DAST misses hardcoded secrets, and SCA misses custom code vulnerabilities.
