AI Security
Griffin AI vs Claude Haiku for Bulk Scanning
Claude Haiku is the cost-efficient model Griffin uses for high-volume scan interpretation. Here's how raw Haiku compares to Haiku inside Griffin's bulk pipeline.
Feb 2, 20266 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.
Claude Haiku is the cost-efficient model Griffin uses for high-volume scan interpretation. Here's how raw Haiku compares to Haiku inside Griffin's bulk pipeline.
Deep reasoning models are transformative for hard logical problems. Security reasoning is only partially a logic problem—the rest is grounding, policy, and workflow.
Distillation compresses the capability of a large model into a small one for a narrow task. For high-volume security workflows, it is often the difference between a working pipeline and an unaffordable one.
You cannot audit what you cannot see. Frontier model training corpora are effectively opaque to their users, and that opacity is not incidental. It shapes what kinds of trust you can extend to the outputs.
Gemini's million-token context window is a genuinely new capability. For security analysis of large codebases, is it enough on its own?
Enterprise agent deployments have moved past pilot phase. The security patterns that have survived contact with production look different from the ones the industry was selling a year ago.
Real exploits cross package boundaries. Griffin AI's graph follows them; Mythos-class tools often stop at the file they are reading.
Weight-level tampering leaves cryptographic and statistical fingerprints. Here is what current research says about detecting a modified checkpoint before it reaches inference.
If your agent can execute code, something it reads from the internet can execute code. Pick your sandbox before the agent picks one for you.
Weekly insights on software supply chain security, delivered to your inbox.