AI Security
Engineer-Hour Savings: Griffin AI vs Mythos
The real cost of a scanner is not the subscription. It is the engineer hours lost to false positives, bad remediations, and noisy queues. We do the math.
Feb 11, 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.
The real cost of a scanner is not the subscription. It is the engineer hours lost to false positives, bad remediations, and noisy queues. We do the math.
What happens when the bug does not match any known CWE? A study of how grounded and pure-LLM scanners perform on genuinely novel vulnerability patterns.
Anthropic's Claude Agent Skills let you package tools and context for Claude. Here's how that primitive compares to Griffin's security-specific workflow scaffolding.
A senior engineer's side-by-side look at Griffin AI and Mythos — why engine-grounded reasoning beats pure-LLM security intuition when the audit clock starts.
Griffin AI v2 brings multi-step reasoning, remediation generation, and deep organizational context to Safeguard's AI engine.
A million-token context window is a tool, not a solution. Context grounding for security requires architecture, not just capacity.
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.
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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