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 container image scanner inspects the layers, packages, and configuration inside an image to find known vulnerabilities before you ship it. Here is how the scan works and what separates a good tool from a noisy one.
A container image vulnerability tool scans the layers, packages, and metadata inside an image so you catch known CVEs before they ship to production. Here is how to pick and use one well.
MCP stands for Model Context Protocol, the open standard that lets AI assistants talk to your tools and data. Here is what it is and where the security risks live.
A practical framework for deciding which AI-enabled tools you should actually adopt, weighed against the data-exposure and supply chain risks they introduce.
An audit trail is only useful if you can answer questions from it. Quality is not about volume — it's about the ability to reconstruct decisions after the fact.
A vulnerability that passes through a working sanitizer is not a vulnerability. Detecting that sanitizer accurately is the difference between actionable findings and noise.
AI Python code correctors fix syntax and obvious bugs fast, but they miss the security and correctness issues that matter most. Here is where they help, where they fail, and how to use them safely.
Where AI-BOM and ML-BOM specifications stand in 2026, which formats have real adoption, and what to capture today even if the standards are still in motion.
Jailbreaking AI means getting a model to bypass its own safety guardrails. This guide explains how jailbreaks work conceptually, how they differ from prompt injection, and how to build layered defenses.
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