AI Security
Air-Gapped Environments: Griffin AI vs Mythos
Air-gapped AI is not a feature flag. It is an architectural commitment, and it separates serious enterprise products from consumer-grade assistants.
Jan 11, 20267 min read
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.
Air-gapped AI is not a feature flag. It is an architectural commitment, and it separates serious enterprise products from consumer-grade assistants.
Tiered models and a deterministic engine cut token consumption to the moments that need reasoning. Pure-LLM tools pay full price for every trivial check.
Most enterprises rolled out AI-for-security tools faster than their governance processes could keep up. The resulting gap is where most of the pain from 2025 deployments lives.
Llama 3 is a powerful open-weight foundation model, but security workflows demand more than raw inference. Here is how Griffin AI compares.
Griffin AI produces draft PRs with taint paths, exploit hypotheses, and disproof attempts. Mythos-class pure-LLM tools skip those anchors, and PR quality suffers.
SBOM adoption has grown rapidly, but maturity varies wildly. Here's where the industry actually stands heading into 2026.
Griffin 3.0 is now generally available. Here is what changed in the reasoning and remediation model, how it behaves in practice, and the defaults you should know.
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.
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