Snyk

Snyk's software composition analysis reads your manifest files (npm, Maven, pip, Go modules, and more) and matches every package against a vulnerability database. Detection is the easy part. Reachability is the hard part. Snyk tells you which of hundreds of alerts actually reach your code through a real import path, so you fix the ones that matter first.

Static analysis inspects the code your team writes for issues like injection flaws and insecure data handling. Snyk's dataflow engine traces how untrusted input moves through functions, which cuts down on false positives that make developers ignore scanner output.

It scans container images, base images, and infrastructure-as-code files such as Kubernetes manifests and Terraform. That means misconfigurations and vulnerable layers get caught before a deploy, not after something breaks in production.

Rather than only describing a problem, Snyk can generate a pull request with the version bump or code change that closes the issue. Developers review and merge it like any other change. No context switch. No separate tool. That keeps the fix inside their normal workflow.

Plugins for VS Code, JetBrains, and Eclipse surface findings while you're typing, and a command-line tool lets you test locally or script scans into a build. Sync, then ship. The point is to shorten the gap between writing code and finding out it's risky.

Newer plans cover AI security specifically: validating AI-written code, governing development agents, and inventorying AI applications running in production. Teams adopting AI coding tools use this layer to keep that speed from turning into unmanaged risk.

Custom security rules, risk-based prioritization, and a REST API let larger organizations fit Snyk into existing reporting and ticketing systems. The API availability matters if you want to feed findings into a dashboard or block a build automatically.