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.
Modern applications use multiple languages and package ecosystems. Analyzing dependencies across these boundaries requires techniques that single-ecosystem tools cannot provide.
DAST finds what source code analysis cannot. Here is how to set it up, tune it, and actually get value from it in a modern CI/CD pipeline.
A defender's glossary of AI security terms — prompt injection, model poisoning, AI-BOM, adversarial ML — with dated, real-world incidents behind each one.
A step-by-step guide to verifying package-lock.json integrity, running npm audit correctly, and scanning Node.js dependencies for tampering and vulnerabilities.
Security isn't just the security team's problem. Building effective collaboration between security, engineering, product, and operations is essential for supply chain defense.
EPSS offers a data-driven approach to vulnerability prioritization. Learn how it works, how it compares to CVSS, and why your team should care.
AI source code analysis pairs LLMs with static analysis to cut false positives and speed triage -- but reachability data still decides what's real.
Brakeman understands Rails conventions and catches security issues that generic scanners miss. Here is how to use it effectively.
How prototype pollution reaches NestJS apps through lodash, qs, tough-cookie, and dotenv-expand — and how Safeguard catches it before it merges.
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