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.
Griffin AI runs on Anthropic's Claude models under the hood. Here's what the engine context, eval harness, and workflow scaffolding actually buy you over calling Claude directly.
Frontier models are remarkable reasoners, but security workflows demand more than raw intelligence. Here's how Griffin AI grounds frontier reasoning in real tenant context.
Reachability-grounded reasoning produces actionable findings. Ungrounded LLM reasoning produces speculation. We explain the methodology gap.
Gemini Pro brings capable reasoning and a massive context window to general-purpose workflows. Griffin AI brings a security engine with an LLM on top. The difference matters when the workflow is appsec.
A detailed comparison of how Griffin AI consumes SBOMs as structured reasoning context while Mythos-class pure-LLM tools skim them as prose — and why that architectural gap determines the quality of every downstream finding.
Griffin AI publishes a five-family eval harness with concrete numbers. Most Mythos-class competitors ask buyers to trust marketing claims instead of data.
A candid look at how Griffin AI's three-stage zero-day pipeline compares to pure-LLM Mythos-class bug hunters, and why false positive rates matter more than raw volume.
Why enterprise AI for security requires genuine on-premises deployment, not just a SaaS endpoint with a VPN in front of it.
Griffin AI moves beyond scan-and-alert to autonomously generate, test, and propose vulnerability fixes. How Safeguard's remediation engine reduces mean time to fix without introducing new risk.
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