A code scanning tool automatically inspects your source code and dependencies for security vulnerabilities, bugs, and policy violations, ideally as part of your normal build so problems surface before they ship. The category spans several distinct techniques — static analysis, dependency scanning, secrets detection — and choosing well means matching the tool type to the risk you're actually trying to catch, then integrating it so it helps developers instead of nagging them. This guide covers what each type does and how to run one without generating noise everyone learns to ignore.
The main types of code scanning tools
"Code scanning" is an umbrella. The tools underneath it look at different things:
SAST (static application security testing) analyzes your own source code without running it, tracing how untrusted data flows through the program to find issues like SQL injection, cross-site scripting, path traversal, and insecure crypto usage. It's language-aware and finds bugs in code you wrote.
SCA (software composition analysis) scans your third-party dependencies — the open-source libraries you pull in — against databases of known vulnerabilities. Since most of a modern application is third-party code, this catches a huge share of real risk that SAST can't, because the flaw isn't in your code at all. An SCA tool also resolves transitive dependencies, so it flags a vulnerable library four levels deep that you never added directly.
Secrets scanning detects credentials — API keys, tokens, private keys — accidentally committed to your repository, ideally catching them before they reach shared history.
IaC scanning checks infrastructure-as-code (Terraform, CloudFormation, Kubernetes manifests) for insecure configuration like public storage buckets or over-broad IAM policies.
DAST (dynamic application security testing) is the runtime counterpart: it probes a running application from the outside, finding issues that only appear when the app is live. A DAST tool complements static scanning rather than replacing it.
Most teams need SAST plus SCA plus secrets scanning at minimum. Which you prioritize depends on where your risk concentrates — heavy dependency use tilts toward SCA first.
SAST vs SCA: catching different bugs
The distinction trips people up, so it's worth being concrete. SAST finds a SQL injection in a query you wrote. SCA finds that the JSON parser you imported has a known deserialization CVE. They don't overlap — one looks inward at your logic, the other outward at your supply chain.
You need both because the two failure modes are independent. A codebase can have flawless first-party code and still ship a critical vulnerability inherited from a dependency, which is exactly what happened to countless teams during the Log4Shell incident. Relying on SAST alone leaves the larger attack surface — your dependency tree — unscanned.
Wiring it into CI without the noise problem
A code scanning tool that isn't in your pipeline is a report nobody reads. The goal is to scan automatically on every change and surface results where developers already work.
# Example: a scan step in a CI pipeline
scan:
stage: test
script:
- run-scanner --fail-on critical,high
# Report results inline on the pull request
A few integration principles separate useful scanning from ignored scanning:
- Scan on pull requests, not just nightly. Feedback at code-review time is acted on; a nightly report that lands in an inbox is not.
- Fail the build only on what matters. Gating on every low-severity finding trains developers to bypass the gate. Start by failing on critical and high, and tighten over time.
- Show findings in context. A result on the PR diff with the vulnerable line and a suggested fix gets fixed. A link to an external dashboard gets ignored.
- Track new vs existing. Block new issues introduced by a change; manage the existing backlog separately so you don't hold every PR hostage to historical debt.
The false-positive problem
The single biggest reason code scanning tools fail in practice is noise. A tool that reports 500 findings, 480 of which are false or irrelevant, is worse than no tool — it teaches your team that scan results are noise to be dismissed.
Managing this is an ongoing job, not a one-time setup:
- Tune and suppress deliberately, with a documented reason for each suppression so it's auditable rather than a black hole.
- Prioritize by reachability and exploitability, not just raw severity. A "critical" in a dependency your code never actually calls is lower priority than a "high" on a directly-reachable path. Modern SCA tools increasingly do reachability analysis to help here.
- Deduplicate across tools so the same issue reported by two scanners doesn't count twice.
A platform such as Safeguard focuses on this correlation problem — merging SAST, SCA, and runtime findings and ranking them by real exploitability — precisely because the raw-finding count is not the useful signal. For a feature-level comparison of how scanning tools differ, see our comparison with Snyk.
FAQ
What is the difference between SAST and SCA?
SAST (static application security testing) analyzes your own source code for flaws like injection and insecure crypto. SCA (software composition analysis) scans your third-party open-source dependencies for known vulnerabilities. They catch independent problems, so most teams run both.
Should a code scanning tool block the build?
Yes, but selectively. Gate the build on critical and high-severity findings — especially new ones introduced by the change — and manage lower-severity and existing issues without blocking every pull request. Gating on everything trains developers to bypass the scan.
How do I reduce false positives from a code scanning tool?
Tune the ruleset to your stack, suppress with documented reasons, prioritize by reachability and exploitability rather than raw severity, and deduplicate findings across tools. Prefer scanners that do reachability analysis so unreachable "critical" findings drop in priority.
Do I need more than one code scanning tool?
Usually yes, because different types catch different risks. A common baseline is SAST plus SCA plus secrets scanning, with DAST added for runtime coverage. Some platforms bundle several of these and correlate their results, which reduces duplicate findings across separate tools.