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.
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.
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.
CycloneDX 1.7 was published in October 2025 and adopted by the General Assembly in December. We unpack what the ML-BOM capability means in practice for AI inventory.
Anthropic's Model Context Protocol introduces a new trust boundary between agents and tools. Here is how the security model actually works in practice.
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.
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.
How Azure Policy enforces container compliance on AKS—registry restriction, regulatory mapping, and where admission-time policy alone falls short.
SQL injection stopped being a single-line bug years ago. Modern chains stitch a tainted parameter through ORMs, caches, background jobs, and downstream services. Griffin AI's engine-plus-LLM architecture follows the taint across those hops; Mythos-class pure-LLM scanners summarise one file at a time and lose the thread.
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.
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