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.
Audit logs are where enterprise AI either proves its seriousness or exposes its improvisation. The gap between Griffin AI and Mythos-class products is visible in the first day of a real audit.
Deep reasoning models are transformative for hard logical problems. Security reasoning is only partially a logic problem—the rest is grounding, policy, and workflow.
Gemini's million-token context window is a genuinely new capability. For security analysis of large codebases, is it enough on its own?
Auto-remediation only scales if human review stays cheap. Griffin AI's grounded PRs keep reviewer time low; Mythos-class PRs push the cost back to humans.
Real exploits cross package boundaries. Griffin AI's graph follows them; Mythos-class tools often stop at the file they are reading.
AI-BOM is how you describe an AI system's supply chain — models, datasets, prompts, inference environments. Griffin AI ingests it as structured inventory. Mythos-class tools try to talk about AI while remaining blind to the AI systems they describe.
A SOC 2 Type II auditor samples a control population across a reporting period. Griffin AI creates that population as a natural output. Mythos-class pure-LLM tools leave you reconstructing it.
An AI security tool that cites the wrong advisory is worse than one that says nothing. Griffin AI benchmarks citation accuracy at 0.89 similarity; Mythos does not.
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