Supply Chain Security, in plain English.

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.

Per-Scan Token Cost: Griffin AI vs Mythos

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.

Griffin AI vs xAI Grok for Security

Enterprise AI Security Rollout: The Governance Gap

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.

Griffin AI vs Llama 3 for Security Workflows

Llama 3 is a powerful open-weight foundation model, but security workflows demand more than raw inference. Here is how Griffin AI compares.

Remediation PR Quality: Griffin AI vs Mythos

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.

CyberSecEval Reviewed: What It Measures

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.

SSDF Attestation: Griffin AI vs Mythos

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 vs Raw Claude for Security Workflow

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.