A code scan tool automatically inspects your source, dependencies, and configuration for security vulnerabilities, leaked secrets, and quality problems — but "code scan tool" covers at least four distinct categories that find different things, and you need more than one to get real coverage. Teams often buy a single scanner, see it miss an entire class of bug, and conclude scanning does not work. The problem is usually that they bought one type of tool for a job that needs several.
This guide explains the categories, how each works under the hood, and how to combine them without burying your engineers in alerts.
The four categories of code scan tool
SAST (Static Application Security Testing) analyzes your source code without running it. It parses code into an abstract syntax tree and traces data flow from untrusted inputs to dangerous operations, flagging injection, path traversal, and similar bugs in code you wrote. It has full coverage of your codebase but produces false positives because it cannot always tell which paths are reachable at runtime.
SCA (Software Composition Analysis) scans your dependencies rather than your code. It reads lockfiles like package-lock.json or pom.xml, builds an inventory of every direct and transitive package, and matches those against vulnerability databases to report known CVEs. Since most of a modern application is third-party code, this catches a huge share of real risk that SAST never sees.
Secrets scanning hunts for credentials committed to the repository — API keys, private keys, database passwords, tokens. It uses regex patterns and entropy analysis to spot high-randomness strings that look like secrets, and it scans git history, not just the current tree, because a secret deleted in a later commit is still recoverable.
DAST (Dynamic Application Security Testing) tests the running application from the outside, sending crafted requests and observing responses. It finds runtime issues — authentication flaws, injection reachable through the actual request path, misconfigurations — with no false positives on what it triggers, but only on code paths it can reach.
No single one of these covers the others. A SAST tool will not find a vulnerable dependency; an SCA tool will not find SQL injection in your own code; neither finds a hardcoded AWS key as reliably as a dedicated secrets scanner.
How each works in practice
SAST tools like Semgrep or CodeQL run rules against your parsed source. Semgrep uses pattern matching that reads almost like the code it matches; CodeQL treats your codebase as a queryable database. You run them in CI on each pull request:
semgrep --config "auto" --error .
SCA tools read your manifests and compare against advisory feeds. A basic version ships with your package manager:
npm audit --audit-level=high
Dedicated SCA platforms go further, tracking transitive dependencies, telling you whether a vulnerable function is actually called (reachability), and suggesting the minimal version bump that clears an advisory. Our SCA product overview covers how reachability analysis cuts the noise.
Secrets scanners like Gitleaks or TruffleHog scan the working tree and history:
gitleaks detect --source . --verbose
DAST scanners crawl the deployed app and probe each endpoint; our DAST overview walks through how a crawl-and-attack cycle works.
Choosing tools without drowning in alerts
The failure mode of code scanning is not missing bugs — it is producing so many findings that developers stop looking. A first SAST run on a mature codebase can emit thousands of results, most of them low priority or false positives. If you gate the build on all of them from day one, the team will route around the tool.
Manage this deliberately:
- Baseline existing findings so the gate only fails on newly introduced issues. Every serious tool supports this. You fix new bugs at the point of introduction and burn down the backlog separately.
- Prioritize by exploitability, not raw severity. A critical CVE in a dependency you do not actually call is lower priority than a medium one on your login path. Reachability data from SCA and confirmed hits from DAST are the strongest prioritization signals you have.
- Fail the build only on high-confidence, high-impact findings. Report the rest without blocking, and review them on a cadence.
Where to run each tool
Placement in the pipeline matters:
- Pre-commit / IDE: secrets scanning and fast SAST rules, so problems never reach the remote at all.
- Pull request CI: full SAST and SCA, gated on new high-severity findings, giving developers feedback while the code is fresh in their heads.
- Post-deploy / staging: DAST against a running environment, since it needs a live target.
- Scheduled: rescan dependencies daily even when code has not changed, because new CVEs publish against packages you already ship.
The last point is easy to forget. SCA is not a one-time check at merge; a dependency that was clean yesterday can have a critical advisory today, so continuous rescanning of already-merged code is essential.
Do not forget IaC and containers
Modern code scan tooling extends beyond application source. Infrastructure-as-code scanners check Terraform, CloudFormation, and Kubernetes manifests for misconfigurations like public S3 buckets or over-permissive IAM. Container scanners inspect image layers for vulnerable OS packages. If your definition of "code" includes your infrastructure and your images — and it should — these belong in the same scanning strategy.
FAQ
What is the difference between SAST and SCA?
SAST analyzes code you wrote, tracing data flow to find bugs like injection. SCA analyzes third-party dependencies, matching them against vulnerability databases to find known CVEs. They cover completely different risk: your own logic versus inherited library flaws. You need both, because each is blind to what the other finds.
Can one tool do everything?
Some platforms bundle SAST, SCA, secrets, and DAST behind one interface, which simplifies procurement and reporting. Under the hood they are still separate engines with separate strengths and weaknesses. A bundle is convenient, but evaluate each engine on its merits rather than assuming a single vendor is best at all four.
How do I stop developers from ignoring scan results?
Baseline the backlog so the build only fails on new issues, prioritize by real exploitability rather than raw CVSS, and keep false positives low by tuning rules. Findings that are timely, actionable, and trustworthy get fixed; a firehose of stale, low-confidence alerts gets muted.
Is free tooling good enough?
Free tools — npm audit, Gitleaks, Semgrep's open rules, OWASP ZAP for DAST — are a genuinely strong starting point and cover the basics well. Commercial tools add reachability analysis, better triage, transitive dependency tracking, and integrated reporting that matter more as scale and compliance requirements grow. Start free, and upgrade where the noise or coverage gaps actually hurt.