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Static analysis identifies security vulnerabilities in source code across 30+ languages like Python, JavaScript, and Go. It uses pattern-based rules to scan for coding standard violations and common flaws, enabling teams to find and fix issues earlier in the development lifecycle.

You can write custom YAML rules to enforce internal coding standards by defining specific pattern-matching or taint-mode logic. This allows you to target specific code patterns and data flows, test the rules locally, and deploy them to enforce your security and quality policies.

Taint-mode analysis tracks data flow from untrusted sources to sensitive sinks to detect injections and data leaks. By configuring these rules in YAML, you can identify when user-controlled input reaches vulnerable functions across your multi-language codebase.

Static code analysis integrates seamlessly into CI pipelines to automate continuous scanning. This provides actionable findings directly within your existing development workflow, allowing you to catch security vulnerabilities and coding standard violations before deployment.

You need specific static analysis tooling installed to perform pattern-based code security checks. The required engine can be installed via package managers like pip or Homebrew, or alternatively run within a Docker container to scan your source code.

The best way to validate custom pattern-matching rules is to run them against test cases using the built-in testing command. This checks your YAML definitions against expected matches and failures, ensuring your rules accurately target specific code patterns before full deployment.