The article introduces LOCALINTEL, a novel automated system for generating organization-specific threat intelligence by integrating global and local cyber knowledge. The system addresses the challenge of manually creating tailored threat response strategies for organizations, which is labor-intensive and error-prone. LOCALINTEL leverages Large Language Models (LLMs) to efficiently process global and local knowledge databases, automating the generation of contextualized threat intelligence. The system operates in three phases: global threat intelligence retrieval, local knowledge retrieval, and contextualized completion generation. It retrieves relevant global threat intelligence from public repositories and local organizational knowledge from internal databases, then fuses these sources to produce a contextualized response. The framework includes modules such as a Global CTI Repository, Local Knowledge Database, Vector Database, Embedding Model, Agent, Tool, and LLM. The Agent orchestrates the retrieval and generation processes, using the ReAct framework to generate queries and execute actions. The system is evaluated through both qualitative and quantitative methods, demonstrating its effectiveness in generating accurate and contextually relevant threat intelligence. LOCALINTEL provides a valuable tool for Security Operations Center (SoC) analysts, enabling them to quickly understand and respond to cyber threats based on their organization's specific context. The system's ability to automate the generation of tailored threat intelligence reduces the reliance on manual efforts, enhancing the efficiency and accuracy of cyber defense strategies.The article introduces LOCALINTEL, a novel automated system for generating organization-specific threat intelligence by integrating global and local cyber knowledge. The system addresses the challenge of manually creating tailored threat response strategies for organizations, which is labor-intensive and error-prone. LOCALINTEL leverages Large Language Models (LLMs) to efficiently process global and local knowledge databases, automating the generation of contextualized threat intelligence. The system operates in three phases: global threat intelligence retrieval, local knowledge retrieval, and contextualized completion generation. It retrieves relevant global threat intelligence from public repositories and local organizational knowledge from internal databases, then fuses these sources to produce a contextualized response. The framework includes modules such as a Global CTI Repository, Local Knowledge Database, Vector Database, Embedding Model, Agent, Tool, and LLM. The Agent orchestrates the retrieval and generation processes, using the ReAct framework to generate queries and execute actions. The system is evaluated through both qualitative and quantitative methods, demonstrating its effectiveness in generating accurate and contextually relevant threat intelligence. LOCALINTEL provides a valuable tool for Security Operations Center (SoC) analysts, enabling them to quickly understand and respond to cyber threats based on their organization's specific context. The system's ability to automate the generation of tailored threat intelligence reduces the reliance on manual efforts, enhancing the efficiency and accuracy of cyber defense strategies.