SAST vs SCA: what each one finds, and why you need both

The short answer

SAST analyses your source code without running it, to find security flaws in the code you wrote. SCA inventories the third-party packages your code depends on, directly and transitively, and checks them against vulnerability and license data. SAST finds the SQL injection in your controller; SCA finds the vulnerable version of the logging library under it.

What SAST is and how it works

Static application security testing parses your code into a structure it can reason about, then applies rules. Simple rules match a dangerous call. Better rules follow data flow: they trace a value from a source an attacker controls (a request parameter, a header, a file) to a sink where it causes harm (a SQL query, a shell, an HTML response), and check whether anything sanitises it on the way.

String id = request.getParameter("id");                          // source: attacker-controlled
String query = "SELECT * FROM orders WHERE id = " + id;
ResultSet rs = conn.createStatement().executeQuery(query);       // sink: SQL injection

PreparedStatement stmt = conn.prepareStatement("SELECT * FROM orders WHERE id = ?");
stmt.setLong(1, Long.parseLong(request.getParameter("id")));
ResultSet rs = stmt.executeQuery();

SAST is good at the bug classes that have a recognisable shape: injection of every kind, cross-site scripting, path traversal, server-side request forgery, insecure deserialisation, weak hashing and encryption, and dangerous APIs. It runs early, on every change, with no running environment.

Its limits are worth knowing:

• It does not understand your business rules. A missing ownership check ("can this user see this invoice?") is usually invisible to it.
• It produces false positives when it cannot prove that input is sanitised or never attacker-controlled. Tune rules and use confidence to triage.
• It does not see runtime configuration, deployed infrastructure or the vulnerabilities inside your dependencies.
• Depth varies by language and framework. Evaluate a tool on representative code from your own stack.

What SCA is and how it works

Software composition analysis starts with an inventory. It reads manifests and lockfiles (package-lock.json, poetry.lock, pom.xml, go.sum and so on), resolves the full tree of direct and transitive dependencies, and matches each package version against vulnerability advisories. Most of the packages in a typical tree are transitive: you never chose them, but you ship them.

<!-- pom.xml -->
<dependency>
  <groupId>org.apache.logging.log4j</groupId>
  <artifactId>log4j-core</artifactId>
  <version>2.14.1</version>   <!-- CVE-2021-44228 (Log4Shell): remote code execution -->
</dependency>

Log4Shell is the textbook case. The flaw was not in anyone's application code, so SAST on that code would never find it. It was in a ubiquitous logging library, often pulled in transitively, and it is listed in CISA's Known Exploited Vulnerabilities catalog. SCA flags it from the version number alone; the fix is to upgrade to a patched release (2.17.1 or later for the follow-up issues).

Beyond vulnerabilities, SCA typically covers:

• Licenses, so a copyleft or unknown license does not reach a product that cannot accept it.
• Malicious and suspicious packages, such as known malware advisories and typosquatted names.
• An SBOM (software bill of materials) in CycloneDX or SPDX format, which customers and regulators increasingly ask for.
• Outdated packages that are not vulnerable yet but are falling behind.

SCA's limits mirror SAST's. It knows nothing about your own code's flaws, it can only report vulnerabilities that are in advisory data, and a vulnerable package is not always an exploitable one: your code may never call the affected function.

How to prioritise findings from each

Both scans can produce long lists. Prioritise with evidence rather than severity labels alone.

SCA: exploit signal first

1. Known exploitation. Is the vulnerability in the CISA KEV catalog (opens in a new tab)? If so, attackers are already using it.
2. Exploit probability. The Exploit Prediction Scoring System (EPSS) estimates the probability of exploitation in the next 30 days.
3. Reachability. Does your code call the vulnerable function, or is the package only present? Treat missing evidence as unknown, not as safe.
4. Severity. CVSS describes how bad exploitation would be. It is the tie-breaker, not the starting point: a critical CVE in an unreachable development dependency outranks very little.

SAST: confidence and reachable input

1. Findings where a data-flow trace shows attacker-controlled input reaching the sink.
2. Findings in code that is exposed: public routes, authentication, payment and file handling.
3. Pattern-only findings, triaged for false positives, with a recorded reason when dismissed.

Running SAST and SCA together

The practical setup is the same for both:

• Scan every pull request, so findings arrive while the author still has context.
• Gate on new findings, not the backlog. Block a merge that introduces a high-confidence SAST issue or a new KEV-listed dependency; fix the existing backlog on a schedule.
• Scan the default branch on a schedule as well. SCA results change when new advisories are published, even if your code does not.
• Record decisions. False positives and accepted risks need a reason and, for accepted risks, an expiry.

Neither scan covers everything. Most programmes add secret detection (keys and tokens in code and git history), infrastructure-as-code scanning (Terraform, Kubernetes, Dockerfiles), container image scanning (OS packages in base images) and, at runtime, DAST (dynamic testing of the running application). If your application calls AI models or agents, add an inventory of those too; see What is an AI-BOM?.

Choosing SAST and SCA tools

Evaluate on your own repositories, not on a vendor's demo project. For each candidate, run the same representative repositories and compare: confirmed findings, false positives, issues you know about that it missed, how clearly each finding shows its evidence, and how much work it takes to act on the results. Check language and ecosystem coverage against what you actually ship, and whether results arrive in the pull request where developers will see them.

CodeStax runs SAST and SCA in the same scan, alongside secret detection, IaC, container and AI attack-surface analysis, for GitHub, GitLab and Bitbucket repositories. SAST findings map to CWE across documented languages including Python, JavaScript, TypeScript, Java, Go, Ruby, PHP, C#, Kotlin, Swift and Rust. SCA covers npm, pip, Maven, Gradle, Go modules, Cargo, Composer, RubyGems and NuGet, prioritises by KEV and EPSS alongside CVSS, and exports SBOMs in CycloneDX or SPDX.