AI-Driven Data Discovery and Analyst Workflow Modernization

This case study outlines the modernization of data discovery and analyst workflows for a global cybersecurity leader using artificial intelligence. The organization faced challenges with its existing data catalog, which lacked essential features such as business glossaries, profiling, and automated testing. As a result, security analysts spent excessive time manually reviewing various documents and data to respond to queries, which hindered decision-making and knowledge leverage. Veltris addressed these issues by integrating data engineering with Generative AI to create a modern discovery and intelligence layer. The solution included rebuilding the internal data catalog to unify sources and developing a ReactJS front end with a Java backend for automated testing. Additionally, a Retrieval-Augmented Generation system was deployed to enhance query responses. The outcomes included a unified self-service catalog, reduced manual document review through AI-driven retrieval, and a scalable foundation for future data growth, ultimately improving analyst productivity and response times.