Supply Chain Security, in plain English.

Griffin AI vs AI21 Jurassic for Security Workflows

Cache Hit Optimisation: Griffin AI vs Mythos

Prompt caching and engine memoisation combine to make Griffin AI scans repeat-cheap. Pure-LLM tools recompute the same reasoning on every run.

Enterprise RAG Security Rollout Antipatterns

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.

CWE Classification Accuracy: Griffin AI vs Mythos

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.

Griffin AI vs Qwen for Code Security

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 Methodology Reviewed

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.

Griffin AI vs Claude Sonnet for Remediation

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 Controls: Griffin AI vs Mythos

Data residency is no longer a procurement checkbox. It is an architectural property that most pure-LLM vendors cannot deliver without major rework.

Domain-Adapted LLMs For Vulnerability Detection in 2026

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.