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.
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.
Pervasive AI means models embedded in nearly every application and workflow. That ubiquity creates a security surface most programs have not mapped. Here is how to think about it.
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.
Workers Builds emits provenance attestations for the code it deploys. We trace how to verify them, gate on them, and integrate them into a multi-cloud supply chain program.
Most platform comparisons stop at features. The API surface is where automation and integration actually happen — and where vendors quietly diverge.
Large language models are being used to find vulnerabilities in open-source code. But a single model, no matter how capable, isn't enough. Here's why multi-agent orchestration, structured CWE analysis, and deep context matter more than model size.
What the EU CRA actually requires from software vendors — SBOMs, vulnerability handling, CE marking, timelines through 2027, and penalties up to EUR 15M.
When SAST beats DAST, when DAST beats SAST, and when you actually need both. A 2026 buyer's decision guide grounded in real program data.
Web app flaws like MOVEit and Log4Shell keep causing breaches. Here's what's actually exploitable in 2026, and how to fix it before attackers do.
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