Consulting and correlating published security events manually is often time-consuming and prone to errors. To address delays in the publication of additional metadata following the release of vulnerabilities, cybersecurity professionals frequently resort to manually leveraging available tools to identify suitable solutions or remain dependent on these sources until new updates are available. This approach often results in inconsistencies and incomplete vulnerability metadata, which can compromise the security posture of various systems. To overcome these challenges, this work presents a real-time approach to curating vulnerability datasets and identifying key trends to assist cyber teams in managing emerging threats. The proposed framework is designed to streamline the handling of security data by connecting directly to the National Vulnerability Database (NVD) as well as multiple sources to gather real-time vulnerability metadata. The system, built around a Python core, manages various tasks and stores results in a local database that continuously updates with recent CVEs and completes missing metadata. Preprocessing scripts apply specific rules to generate a local security database, providing enhanced data for the vulnerability management process. Additionally, the system produces two key outputs: a trend analysis for prioritizing vulnerability management efforts and a dataset that enhances AI models used in vulnerability detection and prediction processes.