SAST vs SCA

Quick comparison

Before diving into details, here is the fundamental difference:

Both are essential. They cover almost entirely non-overlapping risks. Running only one leaves a significant blind spot.

What SAST does

Static Application Security Testing (SAST) analyzes your source code or compiled bytecode to find security vulnerabilities without executing the application. It reads your code, builds an abstract syntax tree, traces how data flows through functions and modules, and flags patterns that indicate security flaws.

SAST catches vulnerabilities that your development team introduces in the code they write:

• Injection flaws — SQL injection, command injection, LDAP injection where user input reaches a dangerous function without sanitization
• Cross-site scripting (XSS) — User-controlled data rendered in HTML without encoding
• Hardcoded secrets — API keys, passwords, and tokens committed to source code
• Insecure cryptography — Deprecated algorithms, weak key lengths, improper use of crypto libraries
• Path traversal — User input used to construct file paths without validation
• Insecure deserialization — Processing untrusted serialized data without type checks

The key characteristic: SAST finds bugs in code that your team wrote. If a developer introduces a SQL injection vulnerability in a new endpoint, SAST catches it. If a developer hardcodes a database password, SAST catches it.

SAST tools vary in depth. Pattern-matching tools like Semgrep are fast and easy to configure. Deep data-flow analysis tools like SonarQube (with commercial editions) trace user input across function calls and files for more complex vulnerability detection. The trade-off is always speed and simplicity versus depth and accuracy.

For a deep dive, see our full guide: What is SAST?

What SCA does

Software Composition Analysis (SCA) identifies and tracks all third-party and open-source components in your application, then checks them against known vulnerability databases and license registries.

Modern applications are 70 to 90 percent open-source code by volume. Your package.json, pom.xml, go.mod, or requirements.txt pulls in dozens of direct dependencies, each of which pulls in their own dependencies (transitive dependencies). A typical Node.js application can have over a thousand packages in its dependency tree. SCA makes that invisible supply chain visible.

SCA catches risks that originate outside your codebase:

• Known vulnerabilities (CVEs) — Library versions with publicly disclosed security flaws
• License compliance issues — GPL-licensed code in a proprietary product, or libraries with incompatible license terms
• Outdated dependencies — Libraries that are no longer maintained or have fallen far behind the current version
• Malicious packages — Typosquatting attacks and compromised packages in public registries

The remediation path for SCA findings is usually straightforward: update the library to a patched version. SCA tools like Snyk Open Source and Dependabot can even generate pull requests with the version bump automatically.

More advanced SCA tools add reachability analysis, which checks whether your code actually calls the vulnerable function in the library. A critical CVE in a library you use might not affect you if you never invoke the vulnerable code path. Reachability analysis reduces noise significantly.

Key differences

Here is a detailed comparison across the dimensions that matter most when choosing and deploying these tools:

The key insight: SAST and SCA have almost zero overlap in what they detect. A SQL injection in your code will never appear in an SCA scan. A known CVE in lodash will never appear in a SAST scan. They are complementary by design.

When to use each

Start with SCA if you have limited security resources and need quick wins. SCA is faster to deploy, produces fewer false positives, and the remediation path (update the dependency) is clear. It also covers the largest attack surface, since most of your code is third-party.

Start with SAST if your application handles sensitive data and your team writes significant custom logic (authentication, authorization, payment processing, data transformation). Custom code that processes user input is where injection flaws, XSS, and logic bugs live.

Use both when you are serious about application security. There is no scenario where running only one provides adequate coverage. The question is sequencing, not selection.

Here is a practical matrix:

Using both together

Running SAST and SCA together provides the most value when they are integrated into the same workflow rather than bolted on separately.

Unified CI/CD pipeline. Run both on every pull request. SCA scans typically complete in seconds; SAST scans take longer but can run in parallel. Both sets of findings should appear as PR comments so developers see all security issues in one place.

Coordinated severity thresholds. Define consistent policies: block merges on critical findings from either tool, warn on high findings, and track medium findings in a backlog. Inconsistent thresholds between tools lead to confusion.

Single dashboard. If you use a platform that bundles both (Snyk, Checkmarx, Sonar), the unified view helps. If you use separate tools (for example, Semgrep for SAST and Snyk Open Source for SCA), consider an ASPM platform to aggregate findings. See our ASPM guide for more on this.

Complementary tools to consider:

The bottom line: SAST secures what you write. SCA secures what you import. Together, they cover the full codebase. Neither alone is sufficient.

FAQ

Frequently Asked Questions

Written by

Suphi Cankurt

Suphi Cankurt is an application security enthusiast based in Helsinki, Finland. He reviews and compares 129 AppSec tools across 10 categories on AppSec Santa. Learn more.