AI Security
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.
Mar 12, 20265 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.
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.
Sometimes a remediation has to be reverted. Griffin AI's minimal, grounded patches roll back cleanly; Mythos-class patches often do not.
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.
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 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.
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 is older than most of the AI tooling trying to detect it. The attacks have adapted to the defences — detection needs to keep up.
Poolside's on-prem code AI is a credible enterprise offering. For security-specific workflows, Griffin AI's grounding architecture targets different ground.
Scanning bursts when a monorepo merges. We explain why Griffin AI absorbs the spike gracefully while Mythos-class tools degrade into rate-limit queues.
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