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.
AI endpoint security uses machine learning to detect threats by behavior rather than signatures. Here is what it actually does, where it helps, and its limits.
AI cybersecurity tools in 2026 split into three real categories — AI-augmented detection, AI-specific application security, and autonomous response — and most vendors only actually cover one.
AI code security solutions range from AI-assisted scanning to AI-generated fixes — here's what to actually test before trusting one with your pipeline.
An AI accelerator is hardware built to speed up machine learning math. Once you offload models onto one, the security work shifts to the software and data around it.
A critical PyTorch RCE bypassed the safety property of torch.load(weights_only=True). We analyze the bug and explain why safetensors should now be the default.
A 4 GB safetensors file deserves the same signing, hashing, and provenance discipline as a container image. How to actually do it with Sigstore, OCI registries, and AIBOMs.
A security-focused buyer comparison of AI coding assistants in 2026: code quality risk, data exfiltration controls, license exposure, and policy enforcement.
New survey data on the state of agentic AI adoption shows enterprises racing to deploy autonomous agents faster than security teams can govern them.
Safeguard's 2026 AI Trust Report surveyed 1,412 developers and finds 91% use AI coding tools weekly, but only 34% trust the code it produces.
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