LOCALINTEL is a novel automated system that generates organization-specific threat intelligence by integrating global and local cyber knowledge. The system is designed to help Security Operations Center (SoC) analysts quickly contextualize global threat intelligence for their specific organizational needs. It consists of three main phases: global threat intelligence retrieval, local knowledge retrieval, and contextualized completion generation. The global phase retrieves threat intelligence from global repositories, while the local phase retrieves relevant knowledge from an organization's private database. The system then combines these sources to produce a contextualized response. The system leverages Large Language Models (LLMs) to process and generate contextualized threat intelligence. It uses a retrieval-augmented generation approach, where global and local knowledge are retrieved and combined to generate accurate and relevant threat intelligence. The system is designed to minimize manual effort, reduce errors, and provide timely, accurate threat intelligence for SoC analysts. The system's architecture includes a global CTI repository, local knowledge database, vector database, embedding model, agent, tool, and LLM. The agent is responsible for retrieving global and local knowledge, generating queries, and producing a contextualized completion. The system is tested with various prompts and demonstrates its ability to generate accurate and relevant threat intelligence. The system was evaluated using qualitative and quantitative methods. Qualitative evaluation showed that LOCALINTEL could generate accurate and relevant threat intelligence by combining global and local knowledge. Quantitative evaluation using the RAGAS framework showed that the system achieved a high score of 0.9535, indicating its effectiveness in generating contextually relevant responses. In conclusion, LOCALINTEL is a valuable tool for SoC analysts, providing a streamlined and automated way to generate organization-specific threat intelligence. It reduces the reliance on manual efforts and enables analysts to focus on critical tasks such as developing defensive strategies against cyber threats. The system's ability to generate accurate and relevant threat intelligence makes it a reliable tool for cybersecurity professionals.LOCALINTEL is a novel automated system that generates organization-specific threat intelligence by integrating global and local cyber knowledge. The system is designed to help Security Operations Center (SoC) analysts quickly contextualize global threat intelligence for their specific organizational needs. It consists of three main phases: global threat intelligence retrieval, local knowledge retrieval, and contextualized completion generation. The global phase retrieves threat intelligence from global repositories, while the local phase retrieves relevant knowledge from an organization's private database. The system then combines these sources to produce a contextualized response. The system leverages Large Language Models (LLMs) to process and generate contextualized threat intelligence. It uses a retrieval-augmented generation approach, where global and local knowledge are retrieved and combined to generate accurate and relevant threat intelligence. The system is designed to minimize manual effort, reduce errors, and provide timely, accurate threat intelligence for SoC analysts. The system's architecture includes a global CTI repository, local knowledge database, vector database, embedding model, agent, tool, and LLM. The agent is responsible for retrieving global and local knowledge, generating queries, and producing a contextualized completion. The system is tested with various prompts and demonstrates its ability to generate accurate and relevant threat intelligence. The system was evaluated using qualitative and quantitative methods. Qualitative evaluation showed that LOCALINTEL could generate accurate and relevant threat intelligence by combining global and local knowledge. Quantitative evaluation using the RAGAS framework showed that the system achieved a high score of 0.9535, indicating its effectiveness in generating contextually relevant responses. In conclusion, LOCALINTEL is a valuable tool for SoC analysts, providing a streamlined and automated way to generate organization-specific threat intelligence. It reduces the reliance on manual efforts and enables analysts to focus on critical tasks such as developing defensive strategies against cyber threats. The system's ability to generate accurate and relevant threat intelligence makes it a reliable tool for cybersecurity professionals.