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.
LLM selection is ultimately a cost-quality optimisation under workflow constraints. The curve is not smooth, and the right point on it depends on where errors land in your pipeline.
Function calling gives models the ability to act. Acting safely on behalf of a specific user, in a specific context, within specific policy is a different problem.
Software licenses fall into a few clear buckets — permissive, copyleft, and proprietary — and knowing which is which decides what you can legally ship. Here is the map, without the legalese.
AI cyber solutions are most useful where they reduce analyst toil: triaging alerts, prioritizing vulnerabilities, and drafting remediation. Here is what to trust and what to question.
Vertex AI Safety is Google's approach to enterprise AI controls. For security-specific workflows, Griffin AI adds grounding the Safety layer doesn't.
Model lock-in is the quiet liability of pure-LLM vendors. Safeguard's bring-your-own-model story gives enterprises the option Mythos-class competitors cannot match.
A function whose output space is finite and enumerable can be secured by testing. A function whose output space is every string of tokens up to some length cannot. That difference quietly invalidates most classical security contracts.
A practical look at rate-limiting patterns for Model Context Protocol servers, covering per-tool quotas, token budgets, burst control, and abuse-resistant designs.
A minimal patch is easier to review, safer to merge, and cheaper to roll back. Griffin AI enforces minimality; Mythos-class tools treat it as optional.
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