Triage Backlog Reduction: Griffin AI vs Mythos
A shrinking triage queue is the clearest sign a security programme is working. We explain why Griffin AI shrinks queues and Mythos-class tools grow them.
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.
A shrinking triage queue is the clearest sign a security programme is working. We explain why Griffin AI shrinks queues and Mythos-class tools grow them.
Fine-tuning an open-weight model sounds like a shortcut to a custom SecOps copilot. In practice, it is one step of a much longer journey.
Asset management AI turns a stale spreadsheet of assets into a living, correlated inventory. Here is what it actually does for security teams and where it falls short.
Nearly every security vendor now claims to be an ai security company — here's how to tell genuine AI-powered security from a rebadged feature, and what to actually evaluate before you buy.
A benchmark that the model has seen in training is a benchmark of memorisation. Specific leakage-testing methods separate generalisation from recall.
Claude Desktop's MCP support makes it a capable security tool. Griffin AI builds on that foundation rather than competing with it.
An architectural comparison of Griffin AI's engine-grounded reasoning stack against the pure-LLM pattern that Mythos-class products rely on.
MCP supports stdio, streamable HTTP, and a handful of experimental transports. Each has distinct security properties, and the choice of transport constrains every other security decision you make about the deployment.
Multi-modal models bring image, audio, and video into the AI supply chain. Each modality introduces provenance and integrity challenges that text-only pipelines never had to face.
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