Snyk

If your team is running AI coding agents in production — Claude Code, Cursor, Windsurf, GitHub Copilot — you’ve probably already felt the gap between traditional AppSec and what these agents actually do. Traditional security tools scan committed code. Agents don’t just write code; they install MCP servers, download skills, run shell commands, and make API calls. By the time a traditional SAST scan runs, the damage is already done. ...

If your team is shipping AI-generated code into production — and let’s be honest, most teams are — you’ve probably noticed the gap. AI agents write code fast, but they also introduce subtle bugs, logic errors, and security vulnerabilities at a rate that manual review can’t keep up with. Sonar’s January 2026 State of Code survey found that AI accounts for 42% of committed code among surveyed developers, yet 96% don’t fully trust AI-generated code, and only 48% always check it before committing. That’s a verification gap, and it’s growing. I’ve spent the last few weeks evaluating the four leading verification platforms that claim to close this gap: SonarQube, Snyk, Aikido, and CodeQL. Here’s what I found. ...

AI-generated code contains security vulnerabilities 3.2× more frequently than human-written code, according to Snyk’s 2026 State of AI Code Security report. That single number explains why the Snyk vs Semgrep debate has sharpened so dramatically over the past eighteen months. Both tools are serious SAST platforms with production deployments at thousands of companies — but they solve the AI-generated code problem with completely different architectural philosophies. Snyk Code uses an ML-based engine (DeepCode AI) that adapts to new LLM-generated patterns without manual intervention. Semgrep uses pattern-based rules with regex-like syntax that you can customize precisely for your codebase. Neither approach is universally better. This guide breaks down where each tool wins, with specific numbers across accuracy, speed, pricing, and IDE integration. ...

AI-generated code contains security vulnerabilities 3.2× more frequently than human-written code, according to Snyk’s 2026 State of AI Code Security report. Static Application Security Testing (SAST) tools that were designed for human-written code are scrambling to keep up with the patterns that LLMs introduce: hallucinated API calls, incomplete error handling, missing authentication checks, and prompt injection surface areas that didn’t exist three years ago. The best tools in 2026 have adapted. Here’s how the top four — Snyk Code, Semgrep, Checkmarx, and Corgea — compare on the dimensions that actually matter for modern development teams. ...