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.
A working engineer's review of CyberSecEval, the Meta-originated benchmark that has quietly become the default sniff test for AI-for-security claims. What it actually measures, what it misses, and how to read its scores without fooling yourself.
The NIST SSDF attestation form asks structured questions with structured answers. A chat transcript is not an answer. We explain how Griffin AI produces the evidence auditors expect, and why Mythos-class tools struggle.
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.
Agents get tool lists, not tool boundaries. We walk through scoping patterns that actually hold when Claude 4 or GPT-5 picks the wrong function at runtime.
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.
Frontier models are general polymaths. Security-specific LLMs are narrow experts. Choosing between them is rarely about raw intelligence and almost always about cost, latency, and the shape of your data.
Non-determinism is not a rough edge frontier labs will polish away. It is an architectural property of how transformer decoding works, and it places a hard ceiling on the kinds of security contracts you can sign.
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.
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