The security of digital systems strongly depends on the accuracy and novelty of vulnerability-related data. While authoritative sources such as the National Vulnerability Database (NVD) and the Common Vulnerabilities and Exposures (CVE) provide standardized references, their content often suffers from outdated or incomplete information, which limits its usability in automated security workflows. At the same time, the increasing reliance on AI-driven solutions and automated vulnerability management processes requires high-quality datasets that can support effective risk mitigation. This thesis presents a modular framework designed to automatically retrieve data from verified sources, analyse associated references through web scraping, and apply Large Language Models (LLMs) to extract and evaluate technical information.