AI Security
Why Engine-Plus-LLM Beats Pure-LLM: Griffin vs Mythos
The structural case for engine-plus-LLM security reasoning — and why pure-LLM products in the Mythos class hit a ceiling that no parameter count can raise.
Feb 25, 20266 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.
The structural case for engine-plus-LLM security reasoning — and why pure-LLM products in the Mythos class hit a ceiling that no parameter count can raise.
One model for every task wastes budget on trivial work. Task-routed architectures match model capability to task requirements — the right lever for security at scale.
A side-by-side security comparison of Windsurf and Sourcegraph Cody: data handling, agent scope, deployment models, and enterprise controls.
Retrieval context poisoning scales differently than direct prompt injection. The attacker's leverage grows with the RAG ingest surface.
Gemini's multimodal capabilities are genuinely useful for some security workflows. For most security workflows, the modality is code and text, not images.
Federal compliance is a long investment, not a marketing claim. Safeguard's FedRAMP HIGH and IL7 readiness is the difference between selling into government and sitting on the outside.
Two years ago, AI vendors shipped without evals. In 2026, the posture has shifted. Customers expect benchmarks. Vendors without them lose deals.
The version a remediation tool picks matters more than the fact that it picked one. Griffin AI grounds its choice in the project; Mythos-class tools do not.
HIPAA's software supply chain expectations have sharpened in 2025-2026. Evidence generation is the difference between passing an audit and rerunning it.
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