Supply Chain Security, in plain English.

What is Auto-Remediation (AI-Powered Fixes)

Auto-remediation uses AI to generate and PR real fixes for vulnerabilities — not just flag them. Here's how it decides what's safe to auto-fix.

Deserialization Vulnerabilities: Griffin AI vs Mythos

Unsafe deserialization looks obvious on a slide and impossible on a real codebase. Sinks are language-specific, gadgets live in third-party libraries, and the tainted byte can arrive wrapped in six layers of framework ceremony. Griffin's engine-plus-LLM design handles each of those concerns separately; Mythos-style pure-LLM scanners blur them into pattern-matching.

AI Agent Security Risks: Why Autonomous Systems Are the Next Supply Chain Frontier

AI agents are consuming APIs, installing packages, and executing code autonomously. The security implications are massive and largely unaddressed.

Griffin AI vs Cohere Command for SecOps

The Disproof Step: Griffin AI vs Mythos

Most AI bug hunters skip the hardest step: trying to kill their own findings. Here is why Griffin AI's disproof pass is the single biggest lever on false-positive rate.

Model Tiering Strategy: Griffin AI vs Mythos

Opus for reasoning, Sonnet for drafting, Haiku for scale. We break down when each tier earns its keep and why single-model architectures cannot compete.

AI Safety Eval Datasets as Supply Chain

The datasets you use to evaluate model safety are themselves a supply chain, and almost nobody is treating them that way. A senior engineer's audit of how eval corpora get poisoned, contaminated, and silently drifted.

Enterprise AI Agent Deployment Lessons, 2026

Lessons learned from a year of enterprise AI agent deployments: what worked, what failed, and what we would do differently starting now.

Griffin AI vs Mistral Large for Remediation

Mistral Large is a strong reasoning model, but remediation is more than generating a diff. We look at what Griffin AI adds for production fix workflows.