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.
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.
How Azure Policy enforces container compliance on AKS—registry restriction, regulatory mapping, and where admission-time policy alone falls short.
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.
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.
Two AI bug hunters can both generate hypotheses. Only one can defend them. A field study of grounded versus ungrounded hypothesis generation in zero-day discovery.
AI models ship with dependencies, use vulnerable libraries, and introduce novel attack surfaces. Traditional scanning is not enough.
Claude Code MCP servers run with the privileges of the developer who invoked them. That makes deployment posture the entire security model.
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