Griffin AI vs Mythos: Architecture Deep Dive
An architectural comparison of Griffin AI's engine-grounded reasoning stack against the pure-LLM pattern that Mythos-class products rely on.
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.
An architectural comparison of Griffin AI's engine-grounded reasoning stack against the pure-LLM pattern that Mythos-class products rely on.
Claude Desktop's MCP support makes it a capable security tool. Griffin AI builds on that foundation rather than competing with it.
A benchmark that the model has seen in training is a benchmark of memorisation. Specific leakage-testing methods separate generalisation from recall.
When to deploy IAST, when to deploy RASP, and when to skip both. A pragmatic decision tree based on application architecture, threat model, and operational maturity.
NIST's National Vulnerability Database nearly stopped enriching CVEs in early 2024, creating a growing backlog that left security teams without the severity scores and metadata they depend on.
A Rocky Linux container scan only produces accurate results when your scanner reads Rocky's own advisory feed instead of guessing from RHEL or CentOS data.
SaaS container security is the set of controls that keep containerized, multi-tenant applications isolated, patched, and hardened from build through runtime. Here is the practical playbook.
A retrospective on the Heroku OAuth token incident, what the public timeline revealed about supply chain trust assumptions, and the durable lessons for platform teams.
Application security consulting services range from a two-week penetration test to a multi-year embedded program, and knowing which one you're buying changes what you should expect to get out of it.
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