Supply Chain Security, in plain English.

AI-BOM Adoption: State of the Art in 2026

The AI Bill of Materials went from concept paper to procurement requirement in under two years. Here is what the current state of the art actually looks like.

Dynamic Dispatch: Griffin AI vs Mythos

Dynamic dispatch hides real exploits behind indirection. Griffin AI models the dispatch; Mythos-class tools guess. That gap changes outcomes.

LLM Jailbreak as a Supply Chain Risk in 2026

A jailbreak in a model you ship downstream is a supply chain incident, not a trivia item. Here is how to reason about it and where the defensive controls belong.

Griffin AI vs Inflection Pi for Security Assistance

Enterprise LLM Budget Management Patterns

LLM spend forecasting is where finance teams meet AI engineering for the first time. The patterns that produce predictability are specific.

Griffin AI vs DeepSeek Coder for Security Review

DeepSeek Coder has become a favourite for code-focused workloads. This is how it compares to Griffin AI when the job is security review, not code generation.

Exploit Path Synthesis: Griffin AI vs Mythos

Finding a bug is not the same as proving it is exploitable. How Griffin AI synthesises concrete exploit paths and why pure-LLM scanners rarely get past the sketch stage.

RAG Pipeline Supply Chain Attacks: Vector DBs and More

RAG pipelines have six or seven supply chain surfaces, and most teams are only watching one. Here is how the attacks actually look in production.

SEvenLLM Design And Coverage

SEvenLLM set out to measure how well LLMs handle Security Event analysis, the unglamorous day-to-day work of SOCs and IR teams. A design review of what the benchmark covers, how it was built, and where the coverage maps or does not map to real operations.