Ais1on/CTI-RAG

CTI-RAG addresses a high-value niche in cybersecurity by moving beyond basic vector-search RAG into Knowledge Graph (KG) and causal-augmented retrieval. In CTI, the relationships between threat actors, malware families, and TTPs (Tactics, Techniques, and Procedures) are structural, making KG-RAG theoretically superior to standard semantic search. However, the project currently has minimal traction (1 star, 0 forks, 0 days old), placing it in the prototype/personal experiment category. The 'causal reasoning' aspect is a sophisticated claim but difficult to implement robustly; without a significant community or proprietary dataset of labeled threat traces, the moat is non-existent. The primary threat comes from established CTI platforms like OpenCTI, which already possess the graph structure and are rapidly integrating LLM layers, or platform giants like Microsoft (Sentinel/Copilot for Security) who have the telemetry data to make causal reasoning actually work at scale. Given the current velocity, this project is highly likely to be superseded by generic GraphRAG implementations or specialized security vendor features within 6 months.