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.
Machine learning classifies malware and odd behavior on devices, but NIST documents poisoning and evasion attacks on such models. See how to use it safely.
AI speeds code review, triage and fixes but adds new risks, from wrong answers to excess agency. Weigh both and keep humans and tests in the loop.
Responsible AI means clear ownership, tested systems and honest limits. See the OECD principles, the NIST AI RMF functions and steps a team can start with.
Before an agent ships, test what it can do, not just what it says. A defensive checklist covering tools, permissions, injection paths and stop controls.
RAG fetches documents at query time. CAG preloads them into the model context and caches them. Compare speed, freshness, access control and failure modes.
Yes. Loading a pickle can execute code, and many .pth model files use pickle. Load only trusted files, prefer safetensors, and scan what you download.
Prompt injection makes an LLM follow attacker text as instructions. It may never be fully fixed, so limit what the model can do and plan for failure.
An agent that browses reads pages written by strangers. This checklist covers isolation, least privilege, approvals, output checks and tests for engineers.
Prompt engineering is writing and testing the instructions you give a model. Teams do it well with success criteria, evals and wary handling of inputs.
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