SSO & SCIM: Griffin AI vs Mythos
Enterprise identity is not a paywall. It is the substrate on which every other security control depends, and it is where Mythos-class vendors quietly fall behind.
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.
Enterprise identity is not a paywall. It is the substrate on which every other security control depends, and it is where Mythos-class vendors quietly fall behind.
Most MCP threat models confuse protocol risk with deployment risk. Here is what the real attack surface looks like after a year of production incidents.
SWE-bench became the default benchmark for measuring AI coding agents, but the security extensions that were bolted on afterwards deserve their own scrutiny. A field review of what they measure, where they break, and whether you should trust the numbers.
Griffin uses Claude Opus as its deepest reasoning engine. Here's what triage looks like with Opus alone versus Opus running inside Griffin's eval harness.
Griffin AI's auto-fixes compile clean 73 percent of the time and pass with minor edits 87 percent. Mythos-class pure-LLM patches rarely show those numbers for a reason.
Fine-tuning teaches a model to be a security expert. Grounding lets a general model act like one by reading the right sources. The right answer is usually both, but the proportions matter.
The EU Cyber Resilience Act wants mandatory vulnerability handling, SBOM delivery, and documented due diligence. Griffin AI produces those artifacts continuously. Mythos-class tools produce conversations about them.
Codex-style coding agents are powerful for writing features. Security remediation needs a different shape of system—one that grounds frontier reasoning in SBOM, policy, and reachability context.
Anthropic's Model Context Protocol introduces a new trust boundary between agents and tools. Here is how the security model actually works in practice.
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