AI Security and Threat Modeling RAG Pack

Every time you ask an AI a question, you're giving it a test. If it doesn't know the answer, it might just make one up. RAG (Retrieval-Augmented Generation) fixes this by giving the AI an open book. It can look up real sources, reference them, and cite its work.

A RAG pack is that open book. A curated, structured collection of source documents, pre-processed and ready to hand to your AI. Instead of hoping the model remembers the EU AI Act or the FDA's guidance on medical device software, you give it the actual text, organized so it can find exactly what it needs.

190 chunks from 5 leading AI security frameworks with 1,335 bidirectional cross-references. Sources include OWASP Top 10 for LLM Applications 2025, MITRE ATLAS, CISA/NSA AI Data Security Guidelines, NIST IR 8596 CSF AI Profile, and NIST AI 100-2e2025 AML Taxonomy.

Domain-specific metadata fields include entity_type, affected_asset, tactical_phase, and security_domain for hybrid search filtering. Built for red teaming tools, security copilots, threat modeling assistants, and vulnerability assessment pipelines.

OWASP-sourced chunks carry CC BY-SA 4.0. All other chunks covered under Commercial Single-Entity License. Full documentation included.