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.
If you use an LLM anywhere in your security program — triage, remediation, detection — you need an eval suite with the same rigor as your test suite. Here is a concrete harness: datasets, thresholds, CI gates, and drift detection.
Pattern-matching scanners miss zero-days by definition. An engine that follows taint across package boundaries plus a model that hypothesizes exploit conditions can find what either would miss alone. Here is how that pipeline works end to end.
Coding agents from OpenAI, Anthropic, and Google are excellent tools. They are also not supply chain security platforms, and the assumption that they can replace one is already producing expensive gaps.
List price is the easiest number to compare and the least interesting one. TCO over three years is where Griffin AI vs Mythos-class platforms actually diverge.
MCP's permissions model is subtle. Here is a careful walkthrough of how tool scoping, sampling, and resource access actually work in production.
Language models are not insecure because of a bug you can patch. They are insecure by construction — non-deterministic, context-poisonable, and unreproducible. Here is how to reason about them without pretending otherwise.
Anthropic's Mythos model is generating buzz for AI-powered vulnerability detection. We break down what it does well, where it struggles, and why security teams should approach the results with healthy skepticism.
Anthropic's Mythos model claims to find vulnerabilities in open-source code using a single LLM. We analyze where this approach falls short and why production-grade zero-day discovery requires Safeguard's Multi-Agent TAOR Deep Think AI Engine.
Most platform comparisons stop at features. The API surface is where automation and integration actually happen — and where vendors quietly diverge.
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