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.
On May 5, 2026, NIST's CAISI signed pre-deployment evaluation agreements with Google DeepMind, Microsoft, and xAI, bringing five frontier labs into a government testing program covering cyber, bio, and chemical risk.
AI security providers fall into a few distinct categories, and picking the right one starts with knowing which risk you are actually trying to cover. This guide breaks down the landscape.
AI information security is the practice of protecting the data that flows through AI systems, training sets, prompts, outputs, and the models themselves, from disclosure, poisoning, and misuse. Here is a working model of the risks and controls.
Tool-using agents have become a viable exfiltration channel. The patterns showing up in incident reports, and the controls that contain them.
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.
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