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 MCP server tells the world what it can do through its capability declaration. Auditing those declarations catches drift, tool poisoning, and misconfiguration before an agent gets talked into using the wrong one.
Prompt caching and engine memoisation combine to make Griffin AI scans repeat-cheap. Pure-LLM tools recompute the same reasoning on every run.
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.
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.
Qwen's open-weight models have strong code benchmarks. We dig into how they compare to Griffin AI when the workflow is real code security, not just leetcode.
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.
Claude Sonnet is the workhorse model Griffin leans on for remediation. Here's how raw Sonnet compares to Sonnet inside Griffin's remediation pipeline.
Data residency is no longer a procurement checkbox. It is an architectural property that most pure-LLM vendors cannot deliver without major rework.
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