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 AI-BOM statically detects MCP client-server connections in source code, what CycloneDX data it captures, and where its coverage stops.
How Snyk's aibom CLI uses static analysis to detect models, agents, and MCP servers, then maps them into a CycloneDX v1.6-compliant ML-BOM structure.
How Snyk's AI-BOM uses code-level analysis, not manifest parsing, to surface shadow AI models, agent frameworks, and MCP servers security teams don't know are running.
How Snyk's AI-BOM API exposes AI model and dataset inventories as queryable, CycloneDX-aligned data teams can pull into CI, GRC, and asset tooling programmatically.
How Snyk's AI-BOM tooling discovers prompt files, SKILL.md packages, and MCP tool chains, and the detection engine it uses to flag prompt-injection risk.
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.
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