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.
As organizations download pre-trained models from Hugging Face and other model hubs, the AI supply chain introduces risks that traditional software security tools don't address.
When an agent can call tools, the permission boundary is no longer between the user and the system. It is between the model's current beliefs and everything the model can reach. That is a much harder boundary to defend.
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.
Large language models have their own supply chains: training data, fine-tuning datasets, model weights, and serving infrastructure. Each layer introduces risk.
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.
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