Natural Language Querying for SIEM Systems: Simplifying Threat Analysis for Security Teams

Abstract

Security Information and Event Management (SIEM) systems are essential for modern cybersecurity, enabling organizations to detect, respond to, and recover from potential threats. Traditional query languages, while powerful, often require specialized knowledge and training, which can slow down response times and complicate threat analysis. This research paper explores the integration of natural language processing (NLP) techniques into SIEM systems, allowing security teams to formulate queries in plain language. We examine the architecture, methodologies, and implications of natural language querying in SIEM, highlighting its potential to enhance situational awareness, reduce cognitive load, and empower security analysts. Our findings suggest that natural language querying can significantly streamline threat analysis, making it accessible to a broader range of users while improving the efficiency and effectiveness of security operations.