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.
LLM selection is ultimately a cost-quality optimisation under workflow constraints. The curve is not smooth, and the right point on it depends on where errors land in your pipeline.
Reasoning models have arrived in security tooling. Evaluating them requires different methodology from evaluating classification or generation models. Here is what good evaluation looks like.
Distillation compresses the capability of a large model into a small one for a narrow task. For high-volume security workflows, it is often the difference between a working pipeline and an unaffordable one.
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.
Fine-tuning teaches a model to be a security expert. Grounding lets a general model act like one by reading the right sources. The right answer is usually both, but the proportions matter.
Frontier models are general polymaths. Security-specific LLMs are narrow experts. Choosing between them is rarely about raw intelligence and almost always about cost, latency, and the shape of your data.
Weekly insights on software supply chain security, delivered to your inbox.