Integrating AI into CVE Identification for Enhancing the SDLC and TLM

The integration of Artificial Intelligence (AI) into Common Vulnerabilities and Exposures (CVE) identification represents a significant advancement in cybersecurity practices, particularly within the Software Development Life Cycle (SDLC) and Threat Lifecycle Management (TLM) frameworks. This article explores the transformative potential of AI technologies, including machine learning, natural language processing, and automated code analysis, in revolutionizing vulnerability management processes. Through a comprehensive analysis of implementation frameworks, quantitative benefits, and organizational challenges, this article demonstrates how AI-enhanced CVE identification can significantly improve detection rates, reduce response times, and optimize resource allocation in security operations. This article examines both technical and organizational considerations, from model accuracy and integration complexity to adoption barriers and training requirements. This article also addresses emerging challenges and future directions, providing valuable insights for organizations seeking to strengthen their security posture through AI-enabled vulnerability management. This article contributes to the growing body of knowledge on AI applications in cybersecurity and offers practical guidelines for implementing AI-driven CVE identification systems within existing SDLC and TLM frameworks.