Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
A technical look at how Snyk's DeepCode AI engine, Agent Fix, and Snyk Studio MCP server scan and govern code from AI coding assistants like Claude Code and Cursor.
How Snyk's Evo AI-SPM extends ASPM's discover-assess-enforce loop to models, datasets, and agents, based on its March 2026 GA launch and public documentation.
A technical look at how Snyk CLI's --json and --sarif output structure vulnerability data, its exit-code quirks, and the official tools that turn it into reports.
A technical look at how snyk-to-html converts Snyk CLI JSON scan output into shareable, self-contained HTML reports for CI pipelines and audits.
Studies show developers trust AI-generated code more than human code, even though it's often less secure. Here's what's driving the AI code trust gap.
Studies show 40-45% of AI-suggested code contains exploitable flaws, and models hallucinate fake packages developers install. Here's what the data says.
AI coding assistants write fast but fail differently than humans do. Learn why scanning AI-generated code needs new heuristics for hallucinated dependencies.
AI now writes up to half of production code. Here is why traditional code review breaks down on AI output, and what teams need to change.
AI writes most new code, but few CI pipelines scan it before merge. Here's why the AI code scanning adoption gap exists — and what closes it.
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