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.
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.
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.
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.
Chain-of-thought helps LLMs with multi-step problems. For vulnerability reasoning, it helps — but only when the chain is grounded in structured evidence.
Shipping AI features without an eval harness is shipping without tests. Here is how to build one that actually gates releases without becoming a bottleneck.
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.
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.
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.
AI security protects models, training data, and agentic systems from prompt injection, poisoning, and unsafe autonomy — here's what it covers and how to build a program.
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