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.
Getting the CWE right is not a taxonomic hobby. It drives remediation, compliance mapping, and detection engineering. Here is how grounded and pure-LLM scanners compare.
Retrieval-augmented generation systems are where enterprise AI meets enterprise data, and where most security rollouts stumble. A catalog of the antipatterns we keep seeing.
Data residency is no longer a procurement checkbox. It is an architectural property that most pure-LLM vendors cannot deliver without major rework.
Claude Sonnet is the workhorse model Griffin leans on for remediation. Here's how raw Sonnet compares to Sonnet inside Griffin's remediation pipeline.
SecBench positioned itself as a comprehensive cybersecurity knowledge and reasoning benchmark for LLMs. A methodology review of its construction, scoring, and the gaps that separate the advertised coverage from what the benchmark actually exercises.
A remediation PR is only useful if it does not break anything else. Griffin AI runs targeted regression before opening; Mythos-class tools usually do not.
GPT-4o is an excellent general-purpose model. Security workflows are a specialty, and specialty work exposes the limits of general intelligence.
Domain adaptation has quietly become the default for LLM-assisted vulnerability detection. A look at what works in 2026, what does not, and what teams should plan for next.
AI code assistants are writing a growing share of production code. The security implications are significant and largely unaddressed.
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