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.
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.
Scanning bursts when a monorepo merges. We explain why Griffin AI absorbs the spike gracefully while Mythos-class tools degrade into rate-limit queues.
AI red teaming is not a one-off exercise. Programmatic red-teaming of AI systems requires specific structure — and most organisations don't have it yet.
Coordinated disclosure with open-source maintainers is a relationship business. Here is what makes it work in 2026, with the artefacts a modern pipeline gives you.
Auth bypasses are rarely a single bug. They live in the interaction between layers — middleware, route handlers, framework annotations. Finding them requires path analysis across abstraction layers.
Claude's prompt caching gives you 90% discount on cached tokens. Security workloads have massive cacheable surface area. Griffin AI takes advantage; direct API use often does not.
ML research has a reproducibility crisis. AI security evaluation inherits it. Vendors publishing numbers that can't be reproduced are the norm — not the exception.
The OpenAI Assistants API is a general agent framework. SecOps needs more than a framework — it needs the engine-grounded reasoning Griffin AI adds on top.
Context-window size matters less than context quality. A look at how Griffin AI's engine-grounded context beats pure-LLM retrieval at monorepo scale.
Frontier model pricing is rising even as cheaper alternatives proliferate. The 2026 architectural response is multi-tier model routing — and the security implications are non-trivial.
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