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.
AI cybersecurity software uses machine learning to spot anomalies, triage alerts, and prioritize risk faster than humans can. Here is what it genuinely helps with and where it still needs a human.
When a vendor's incident affects you, the coordination work between their IR team and your ops becomes its own project. How to run it well.
Field notes on running Tetragon, Falco, and Cilium eBPF controls in production Kubernetes clusters, with observed overhead, policy traps, and kernel constraints.
A vulnerability that passes through a working sanitizer is not a vulnerability. Detecting that sanitizer accurately is the difference between actionable findings and noise.
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.
Bedrock Guardrails now span prompt filtering, contextual grounding checks, and tool-use policies. We trace how they fit into a supply chain threat model for production agents.
From AI-generated code risks to regulatory enforcement and package manager security evolution, here are the trends that will define software supply chain security in 2025.
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.
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