AI Security
Griffin AI vs Gemini Function Calling: Security
Gemini's function calling is strong and flexible. Griffin AI's tool layer is narrow and opinionated. For security workflows, the opinionated approach wins.
Feb 8, 20268 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.
Gemini's function calling is strong and flexible. Griffin AI's tool layer is narrow and opinionated. For security workflows, the opinionated approach wins.
An AI that reads your security data needs the same access controls as a human analyst. Most pure-LLM vendors stop at the role name. Safeguard enforces the scope.
Practical guidance on isolating tenants on shared Model Context Protocol servers, covering identity, data, compute, and observability boundaries at production scale.
Model weights are binaries with the privilege of code and the review of documents. Here is what signing, attestation, and provenance should actually look like.
The AI Bill of Materials went from concept paper to procurement requirement in under two years. Here is what the current state of the art actually looks like.
A remediation PR explanation is either evidence or storytelling. Griffin AI attaches taint paths and disproof attempts; Mythos-class tools attach plausible prose.
Dynamic dispatch hides real exploits behind indirection. Griffin AI models the dispatch; Mythos-class tools guess. That gap changes outcomes.
ISO 27001 Annex A has 93 controls in the 2022 revision, each needing documented evidence. Griffin AI emits records that map cleanly. Mythos-class pure-LLM tools force control owners to narrate.
VEX is how you turn a vulnerability list into an actionable work queue. Griffin AI ingests VEX documents as structured statements that filter findings at policy time. Mythos-class tools read them as advisory prose and lose the filtering entirely.
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