International Journal of Scientific Research and Engineering Development

Organizational networks today are grappling with two major challenges namely rising vulnerability risks and the need to meet increasing performance demands. This calls for solutions that go beyond the usual security and optimization methods. Traditional vulnerability assessment techniques, like static scanning and penetration testing, often struggle to keep up with evolving threats. Meanwhile, standalone optimization models find it tough to strike a balance between efficiency and resilience. To tackle these issues, this paper introduces a hybrid AI framework that combines advanced machine learning, natural language processing, and graph-based modeling for vulnerability detection, along with reinforcement learning and evolutionary algorithms for network optimization. The framework taps into a variety of data sources, including traffic logs, system configurations, and vulnerability repositories, to provide proactive risk scoring, predict attack paths, and develop adaptive optimization strategies. Through thorough evaluation on benchmark datasets and simulated organizational environments, the framework shows improved accuracy in spotting exploitable vulnerabilities while also enhancing network throughput, cutting down latency, and optimizing resource allocation. This integrated approach not only bolsters security but also ensures operational efficiency, allowing organizations to build resilience against cyber threats while delivering high-quality services.

Dr. Deepak Tomar, Dr. Kismat Chhillar, Prof. Saurabh Shrivastava,"A Hybrid AI Framework for Vulnerability Assessment and Network Optimization in Organizational Applications" International Journal of Scientific Research and Engineering Development, V9(2): Page(644-652) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.