SAST vs SCA: Which Code Scanning Do You Need?
Modern applications are built from two distinct types of code: the code your team writes and the open-source dependencies your code relies on. SAST (Static Application Security Testing) finds bugs in your code. SCA (Software Composition Analysis) finds bugs in your dependencies. Both are essential, but they work differently, find different things, and have different strengths and limitations. This guide breaks down what each approach does, how they compare, and why you need both.
What Is SAST? Finding Bugs in Your Code
Static Application Security Testing analyses your source code without executing it. SAST tools read your codebase, build an abstract syntax tree (AST) or control flow graph, and then apply rules to identify patterns that indicate security vulnerabilities. Think of it as a code reviewer that specialises exclusively in security and never gets tired or distracted.
SAST tools detect vulnerabilities that your developers introduce in the code they write. This includes SQL injection through string concatenation, cross-site scripting from unsanitised output, path traversal from unvalidated file paths, command injection from user input passed to shell commands, hardcoded secrets and credentials, insecure cryptographic implementations, and dozens of other patterns that represent security risks.
The leading open-source SAST tool is Semgrep, which supports over 30 programming languages and provides a rule engine that allows custom pattern matching. For a detailed look at how SecPortal integrates Semgrep, see our SecPortal vs Semgrep comparison. Unlike older SAST tools that required full compilation of the target codebase, Semgrep operates directly on source code and can scan individual files or entire repositories without a build step. This makes it fast enough to run in CI/CD pipelines without significantly slowing down deployments.
SAST scans are deterministic: the same code always produces the same findings. This makes them reliable for regression testing and baseline tracking. When a finding is fixed, subsequent scans confirm the fix. When new code introduces a vulnerability, the next scan catches it.
What Is SCA? Finding Bugs in Your Dependencies
Software Composition Analysis examines the third-party libraries and packages your application depends on. Instead of analysing your source code, SCA tools read your dependency manifests (package.json, requirements.txt, Gemfile.lock, go.sum, pom.xml) and compare the declared versions against vulnerability databases like the National Vulnerability Database (NVD), GitHub Advisory Database, and OSV.
The premise is straightforward: if your application uses version 4.17.15 of lodash and that version has a known prototype pollution vulnerability (CVE-2020-8203), SCA flags it and tells you which version fixes the issue. SCA tools also detect transitive dependencies, the libraries that your direct dependencies depend on, which often contain their own vulnerabilities that are invisible when you only look at your direct dependency list.
Common SCA tools include npm audit for Node.js, pip-audit for Python, bundler-audit for Ruby, and language-agnostic tools like Trivy, Grype, and Snyk. If you are evaluating SCA options, see our SecPortal vs Snyk comparison. These tools are fast because they are comparing version strings against a database rather than analysing code patterns. A full SCA scan of a large project typically completes in seconds.
SCA is critical because modern applications are overwhelmingly composed of third-party code. Studies consistently show that 80-90% of the code in a typical application comes from open-source dependencies. A single vulnerable dependency can expose your entire application to attack, regardless of how securely your own code is written.
Head-to-Head Comparison
SAST analyses the source code your team writes. SCA analyses the third-party packages your code depends on. They look at completely different parts of your application, which is why neither can replace the other.
SAST finds injection flaws, XSS, insecure deserialization, hardcoded secrets, and logic errors in your code. SCA finds known CVEs in dependencies, outdated packages with available patches, and license compliance issues. There is almost no overlap between the vulnerability classes each approach detects.
SAST tools historically suffer from higher false positive rates because pattern matching on source code cannot always determine whether a flagged pattern is actually exploitable in context. Modern tools like Semgrep have improved significantly by supporting more precise rule definitions and dataflow analysis. SCA has lower false positive rates because it is matching exact version numbers against known vulnerabilities, though not every vulnerable dependency version is actually exploitable in every usage context.
SAST tools need language-specific parsers and rules. Semgrep supports 30+ languages but the depth of rules varies by language. SCA tools need to understand package manager formats. Most SCA tools support the major ecosystems (npm, PyPI, Maven, RubyGems, Go modules) comprehensively, with sparser coverage for niche package managers.
Both SAST and SCA are fast enough for CI/CD integration. SCA is typically faster (seconds) because it only reads manifests. SAST takes longer (seconds to minutes depending on codebase size) because it parses and analyses source files. Both can be configured as pipeline gates that block deployments when critical findings are detected.
SAST: Strengths and Limitations
- Finds vulnerabilities in code before it reaches production
- Catches security anti-patterns that code review might miss
- Custom rules can enforce organisation-specific security standards
- Deterministic results enable reliable regression tracking
- No running application required; works on source code directly
- Cannot detect runtime vulnerabilities or configuration issues
- Higher false positive rates than SCA, requiring manual triage
- Rule quality varies significantly between languages and frameworks
- Cannot determine if a code pattern is actually reachable and exploitable
- Does not assess third-party dependency security
SCA: Strengths and Limitations
- Fast and lightweight, completing scans in seconds
- Low false positive rate due to exact version matching against CVE databases
- Covers transitive dependencies that developers may not be aware of
- Clear remediation path: update to the patched version
- Can also detect licence compliance issues in dependencies
- Only finds known vulnerabilities with published CVEs; zero-days are invisible
- Cannot determine if a vulnerable dependency is actually used in a way that is exploitable
- Dependency updates can introduce breaking changes, making remediation non-trivial
- CVE database coverage varies; some ecosystems have better reporting than others
- Does not find any vulnerabilities in your own code
Why You Need Both: The Complete Code Security Stack
SAST and SCA are complementary, not competing, technologies. Running only SAST means you are blind to the 80-90% of your application that comes from dependencies. Running only SCA means you are ignoring the security of the code your team actually writes. Neither alone provides adequate code-level security coverage.
The recommended approach is to run both in your CI/CD pipeline as part of a DevSecOps scanning workflow. Configure SCA to run on every pull request since it is fast and has low noise. Configure SAST to run on pull requests as well, with rules tuned to reduce false positives so that developers do not learn to ignore the results. Set severity thresholds that block merges for critical and high findings while allowing lower severity findings to be tracked and addressed in subsequent sprints.
For security consultancies assessing client codebases, running both SAST and SCA provides the most comprehensive code review coverage. SAST findings demonstrate vulnerabilities in the client's own code with specific line references and remediation guidance. SCA findings highlight supply chain risk from outdated or vulnerable dependencies. Together, they paint a complete picture of code-level security posture.
When combined with runtime scanning (both authenticated and unauthenticated), you achieve security coverage across the entire application lifecycle: code issues are caught during development by SAST and SCA, and runtime issues are caught in staging and production by DAST and external scanning. This defence-in-depth approach is what mature DevSecOps programmes implement.
Practical Implementation Tips
- Start with SCA if you can only adopt one tool first, as it provides the highest value with the lowest friction
- Add SAST once your team is comfortable triaging SCA findings and has established a findings management workflow
- Tune SAST rules aggressively to reduce false positives; a noisy scanner that gets ignored is worse than no scanner
- Track findings over time to measure whether your codebase security posture is improving or degrading
- Integrate scan results into your findings management workflow for consistent tracking alongside manual assessment findings
- Schedule regular full scans in addition to PR-triggered scans to catch newly disclosed CVEs in existing code
- Use code scanning results to inform your continuous monitoring programme
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