griffin-ai
Safeguard articles tagged "griffin-ai" — guides, analysis, and best practices for software supply chain and application security.
180 articles
Support Model: Griffin AI vs Mythos
Support tier comparisons look identical on paper. The real difference shows up at 2am during an incident, and the shape of that difference is worth understanding before signing.
Rollback Safety: Griffin AI vs Mythos
Sometimes a remediation has to be reverted. Griffin AI's minimal, grounded patches roll back cleanly; Mythos-class patches often do not.
Zero-Day Discovery Economics: Cost Per Find
The economics of zero-day discovery have been opaque for too long. Here is the actual cost structure of finding a real, defensible bug, and how to think about it.
CMMC Pass-Through: Griffin AI vs Mythos
CMMC 2.0 rollout has made flow-down expectations concrete. AI-for-security tools used by DIB contractors are in scope, and the pass-through story matters.
Transitive Depth: Griffin AI vs Mythos
Most scanners stop at five or six levels of transitive depth. Real production graphs run sixty levels deep, and the most interesting vulnerabilities live in the long tail.
Training Data Provenance: Griffin AI vs Mythos
Training data is a supply chain component. Knowing what went into a model is the precondition for knowing what could come out of it. Few tools track this; the few that do matter disproportionately.
Remediation Prioritisation With Reachability And EPSS
CVSS alone is a bad prioritisation signal in 2026. Reachability plus EPSS gives teams a defensible order to fix the vulnerabilities that actually matter.
Cost Per Finding: Griffin AI vs Mythos
Token spend per scan is the wrong metric. Cost per actionable finding is the right one — and it's where engine-plus-LLM economics dominate pure-LLM economics.
Dependency Confusion: Griffin AI vs Mythos
Dependency confusion is older than most of the AI tooling trying to detect it. The attacks have adapted to the defences — detection needs to keep up.
Griffin AI vs Poolside for Enterprise Security
Poolside's on-prem code AI is a credible enterprise offering. For security-specific workflows, Griffin AI's grounding architecture targets different ground.
Elastic Scale Behaviour: Griffin AI vs Mythos
Scanning bursts when a monorepo merges. We explain why Griffin AI absorbs the spike gracefully while Mythos-class tools degrade into rate-limit queues.
Griffin AI vs Open Weights: The Eval Gap
Frontier models pass eval benchmarks that open-weight models miss by specific measurable margins. For security workflows, the gap matters.