Improving Cybersecurity Threat Sharing Systems with Knowledge Graphs Driven by AI

Abstract

Companies in detecting, evaluating, and mitigating newly developing risks rely significantly on sharing platforms for cybersecurity threat intelligence (CTI). Conventional sharing systems might therefore suffer from data silos, inefficiencies, and challenges to provide valuable insights. Rising as a revolutionary tool to go above these constraints, AI-powered knowledge graphs provide enhanced decision-making, dynamic and organized threat depiction, and real-time updates. Emphasizing their capacity to provide effective data integration, contextual threat analysis, and proactive defensive systems, this work investigates how knowledge graphs driven by artificial intelligence may be included into CTI sharing systems. We research the building of these knowledge networks along with useful applications and problems using graph-based approaches, natural language processing, and machine learning. Case studies show how well knowledge graphs driven by artificial intelligence increase company cooperation and threat intelligence sharing. The results reveal how successfully this technology may turn CTI systems into more strong, flexible, useful tools.