AI Security
Onboarding Velocity: Griffin AI vs Mythos
Time from contract signature to first meaningful finding is the metric procurement cares about. Griffin AI and Mythos-class tools diverge in week one.
Mar 4, 20263 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.
Time from contract signature to first meaningful finding is the metric procurement cares about. Griffin AI and Mythos-class tools diverge in week one.
Retrieval-augmented generation was the 2024 success story. 2026 is when RAG poisoning moved from research to production incidents.
A vulnerable transitive dependency may require upgrading an ancestor. Griffin AI computes the cascade; Mythos-class tools often stop at the first level.
EU AI Act enforcement began in 2026. Vendors sold as "AI security tools" are now high-risk systems with documentation obligations. The shape of the documentation matters.
A vulnerability in version 1.2.0 may not affect your 1.3.5 install if the fix reshaped the call signature. Version-aware resolution is where deterministic engines beat pure-LLM heuristics.
MCP servers are privileged dependencies. An inventory that tracks them like SBOM tracks packages is the minimum bar — and not every tool meets it.
Autonomous coding agents can escalate privilege in subtle ways that traditional threat models miss. A breakdown of the common escalation paths and how to constrain them.
GitHub Copilot suggests fixes. Griffin AI generates fix PRs with taint paths and disproof attached. The difference is review burden.
Evals that run once are marketing. Evals that run on every build are infrastructure. Griffin AI runs the harness on every change; Mythos does not describe one.
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