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.
Build a pre-install guard that catches typosquatted npm, PyPI, and RubyGems dependencies using Levenshtein distance, download-count heuristics, and registry APIs.
Components do not stay secure forever. This guide covers managing the full lifecycle of software dependencies -- from adoption through deprecation -- with a focus on security and operational continuity.
GuardDuty's extended threat detection correlates findings across signals into attack sequences. We dig into where it helps, where it misses, and how to wire it into supply chain incident response.
AI incidents are not the same shape as traditional security incidents. The playbooks need to be specific to how AI systems actually fail.
Gemma is built for efficiency. Can a small open-weight model replace Griffin AI for lightweight scanning workflows, or does the engine still matter?
The real cost of a scanner is not the subscription. It is the engineer hours lost to false positives, bad remediations, and noisy queues. We do the math.
What happens when the bug does not match any known CWE? A study of how grounded and pure-LLM scanners perform on genuinely novel vulnerability patterns.
Adopting an open source dependency is a trust decision. This guide provides a structured methodology for evaluating the security posture of open source projects before adding them to your supply chain.
The most grounded concerns about AI are not sci-fi scenarios; they are prompt injection, data leakage, supply chain risk in models, and opaque decisions. Here is how each one actually shows up.
Weekly insights on software supply chain security, delivered to your inbox.
Your first fix PR is minutes away.
No sales call required, even your agent can complete the purchase over MCP.
