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 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.
The context window is usually marketed as a capability parameter. In a security setting, it behaves like a budget, a forgetting function, and an attack surface all at once.
Gemini Ultra sets a high bar on complex reasoning benchmarks. But security reasoning is not benchmark reasoning. Here's how Griffin AI's engine-first approach changes the outcome.
CycloneDX is not a text format to be summarized — it's a typed graph with dozens of semantically-rich fields. Griffin AI consumes it as a graph. Mythos-class tools consume it as tokens. That difference decides every downstream finding.
Regulators across three continents are converging on a single demand: show where your training data came from. The engineering implications are larger than most labs have admitted.
Shallow call graphs miss real exploits; deep graphs surface them. We examine how Griffin AI and Mythos-class tools differ on depth, and why it matters.
A benchmark number is only as good as the methodology that produced it. Here is how Griffin AI builds its harness and why most Mythos-class tools cannot be audited.
Weekly insights on software supply chain security, delivered to your inbox.