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.
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.
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.
Vertex AI Safety is Google's approach to enterprise AI controls. For security-specific workflows, Griffin AI adds grounding the Safety layer doesn't.
The first enforcement window under the EU AI Act has closed. The actual pattern of enforcement looks different from the one vendors and advocacy groups predicted.
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.
A practical look at rate-limiting patterns for Model Context Protocol servers, covering per-tool quotas, token budgets, burst control, and abuse-resistant designs.
MCP servers connect AI agents to your infrastructure. Here's how to secure them without killing the productivity gains.
AI/ML models are the new open source libraries. Here's why your supply chain security strategy needs to account for model provenance, poisoning, and compliance.
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