The text discusses the potential of Large Language Models (LLMs) in supporting intelligence analysis and the generation of expert-driven insights. It emphasizes the need for appropriate safeguards and verification procedures when utilizing LLMs for fact-checking or information gathering. The text explores the development of custom knowledge extraction pipelines and the creation of Subject Matter Expert (SME)-driven knowledge graphs using LLM copilot capabilities. The ultimate objective is to inspire tech-savvy Open Source Intelligence Analysts to construct their own OSINT toolkits with the support of LLM coder copilots. The text also highlights the advancements in NLP sciences and the opportunities they present for intelligence analysts.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Leveraging LLMs for OSINT analysis | Adoption of LLMs in OSINT workflow | OSINT analysts using LLM-powered tools | Improved efficiency and accuracy |
| Custom knowledge extraction pipelines | Development of custom pipelines | More efficient and accurate knowledge extraction | Enhancing analyst workflows |
| Creation of Subject Matter Expert-driven knowledge graphs | Utilizing knowledge graphs | Improved traceability and accuracy of information | Enhancing analyst workflows |
| Inspiring new generation of tech-savvy Open Source Intelligence Analysts | Encouraging analysts to build their own toolkits | More self-sufficient and skilled analysts | Advancement in technology and techniques |
| NLP-powered engines for intelligence analysis | Integration of NLP in intelligence workflows | Faster analysis and report generation | Advancement in NLP and computing technologies |
| Progress in NLP sciences revolutionize intelligence analysis | Revolutionizing intelligence analysis workflows | Improved efficiency and accuracy in analysis | Advancements in NLP and computing technologies |
| Commercialization of cloud technologies | Availability of cloud technologies | Increased accessibility and scalability | Advancements in cloud computing |
| Creation of transformer models and LLMs | Improved performance and accuracy | Enhanced language understanding and context | Advancements in transformer models and computing technologies |
| Utilizing LLMs for NLP-driven intelligence workflows | Integration of LLMs in intelligence workflows | Enhanced efficiency and accuracy in intelligence analysis | Advancements in LLM technology and techniques |