AI Security
Griffin AI vs Open Weights: Supply Chain Risks
Open-weight models give you total deployment control. They also give you a new supply chain to secure. The tradeoff is worth being explicit about.
Mar 23, 20263 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.
Open-weight models give you total deployment control. They also give you a new supply chain to secure. The tradeoff is worth being explicit about.
Claude's citations feature makes the model say where its claims come from. Griffin AI uses it for advisory workflows where traceability is the entire point.
Per-token pricing on the OpenAI API looks cheap on a single call and expensive on a year-long security workload. Griffin AI's pricing reflects the architecture.
A zero-day discovery pipeline is only as useful as the triage process around it. Here is what triage looks like when the pipeline gives engineers something they can defend.
Gemini on-device models are fast and cheap. For the developer-tool layer, they're useful. For the engine-plus-LLM layer, on-device is not the right fit.
The difference between grounded reasoning and hallucinated reasoning is not eloquence — it's citation. A look at how Griffin AI anchors every claim.
Reviewers trust fix PRs that come with evidence. Here is how to attach the right evidence so AI-assisted remediation gets approved on the first pass.
An auto-fix that closes a vulnerability and breaks the build is not a fix. Breaking-change awareness separates auto-PRs that ship from auto-PRs that get reverted.
An audit trail is only useful if you can answer questions from it. Quality is not about volume — it's about the ability to reconstruct decisions after the fact.
Weekly insights on software supply chain security, delivered to your inbox.