To check code effectively for security, you layer four kinds of automated checks — linting, static analysis, secret scanning, and dependency scanning — and put human review on top of them, because no single tool finds everything. When someone says "check my code," they usually mean one of two things: does it work, and is it safe? This guide is about the second. A code check that only runs a formatter tells you the indentation is consistent; it says nothing about the injection bug on line 40. Here is the workflow that actually catches security issues before they reach production, and how to keep the noise low enough that people pay attention.
Start by knowing what you are checking for
A useful code check separates concerns instead of treating every finding as equal. There are four broad categories worth checking, roughly in order of how much they matter for security:
- Correctness and style — the code quality check layer: dead code, unused variables, inconsistent formatting. Low security value, high readability value.
- Security patterns — dangerous function calls, injection points, missing authorization, unsafe deserialization.
- Secrets — API keys, tokens, and passwords committed to the repository.
- Dependencies — known vulnerabilities in the packages you pull in.
Most teams over-invest in the first category and under-invest in the last three, which is exactly backwards from where breaches come from. When you set out to check a code base, weight your effort toward the layers that carry real risk.
Layer one: lint and format
Linting is the fastest feedback and the right first gate, even though its security value is modest. A linter enforces consistency and catches a surprising number of latent bugs — unreachable branches, comparison mistakes, shadowed names. Run it on save and in a pre-commit hook so nothing reaches review with obvious defects. This is the baseline code quality check, and its real job is to clear the trivial stuff out of the way so human reviewers can focus on logic and security.
Layer two: static analysis (SAST)
This is where checking code becomes checking security. A static application security testing tool parses your source and looks for known-dangerous patterns without running it: SQL built by string concatenation, command execution with user input, unsafe deserialization, path traversal, weak cryptography. Tools like Semgrep (multi-language, and you can write your own rules) and language-specific scanners (Bandit for Python, gosec for Go, the built-in analyzers in many IDEs) live here.
The trap with SAST is noise. Run it with default rules against a mature codebase and you get thousands of findings, most of them low-value, and the team learns to ignore the whole thing. The fix is to gate on new findings only — fail the build when a pull request introduces a high-severity issue, and let the historical backlog be a separate, scheduled cleanup. A check that stays green on clean code and lights up precisely on regressions is a check people trust.
Layer three: scan for secrets
The single highest-return check for most teams is secret scanning, because a leaked credential is an immediate, exploitable problem with no analysis required. Tools like gitleaks and trufflehog scan your history and your diffs for anything that looks like a key or token:
# scan the working tree and staged changes
gitleaks protect --staged
# scan the full history
gitleaks detect --source .
Wire this into a pre-commit hook so a secret is caught before it ever lands in a commit — because once a credential is in git history, rotating it is the only real remedy, and rotation is expensive. Checking code for secrets before commit is far cheaper than the incident that follows a leaked production key.
Layer four: check your dependencies
The code you wrote is a fraction of what ships. Most exploitable vulnerabilities today live in third-party packages, so any serious effort to check code has to include the dependency tree. Language-native tools give you a baseline — npm audit, pip-audit, mvn dependency-check — and a dedicated software composition analysis tool adds transitive-dependency tracking and reachability analysis, so you patch the CVE your code path actually invokes instead of every one in the tree. This layer is easy to skip because it feels separate from "checking my code," but it is often where the real risk concentrates.
Layer five: human review
Automated checks find pattern-matchable bugs. They do not find broken authorization logic, a business rule that lets one user read another's data, or a subtle race condition. That is what human code review is for. The most effective reviews happen when the automated layers have already cleared the noise, so a reviewer spends their attention on logic and design rather than pointing out formatting. Give reviewers a short security checklist — does this touch auth, does it handle user input, does it add a dependency, does it log anything sensitive — so the review consistently checks the things that matter.
Putting it in CI
A practical pipeline runs the cheap checks first and the expensive ones as gates:
# conceptual CI order
1. lint + format # instant, blocks trivial defects
2. secret scan # fast, blocks on any hit
3. SAST # blocks on new high-severity findings
4. dependency scan # blocks on new known vulnerabilities
5. human review # required approval before merge
The ordering matters: put the fastest checks first so most failures surface in seconds, and reserve build-blocking for the security-relevant layers. If you want to go deeper on individual layers, our academy breaks down each type of scanning and how to tune it.
FAQ
What is the fastest way to check code for security issues?
Start with secret scanning — it is fast, needs no analysis, and a leaked credential is immediately exploitable. Then layer SAST for code patterns and a dependency scan for known-vulnerable packages.
Is a linter enough to check my code?
No. A linter is a code quality check that catches style and some latent bugs, but it does not find security issues like injection, broken authorization, or vulnerable dependencies. Use it as the first layer, not the only one.
How do I check code without drowning in false positives?
Gate on new findings only. Fail the build when a change introduces a high-severity issue, and treat the historical backlog as separate scheduled work, so the check stays green on clean pull requests and people keep trusting it.
Can automated tools replace human code review?
No. Automated checks find pattern-matchable bugs; they miss broken business logic, authorization flaws, and design issues. The best results come from automation clearing the noise so human reviewers focus on logic and security.