Proactive Cybersecurity and Threat Detection Using Predictive AI for Resilient Digital Enterprise Ecosystems

Abstract

The rapid expansion of digital enterprise ecosystems has increased the complexity, scale, and sophistication of cybersecurity threats. Conventional security approaches that primarily depend on predefined signatures, static rules, and reactive incident response are increasingly challenged by zero-day attacks, advanced persistent threats, insider risks, automated attacks, and rapidly changing cloud infrastructures. This research proposes a predictive artificial intelligence (AI)-driven cybersecurity framework designed to strengthen proactive threat detection and resilience across interconnected digital enterprise environments. The proposed approach integrates machine learning, behavioral analytics, anomaly detection, predictive modeling, threat intelligence, and automated risk assessment to identify emerging security threats before they develop into major incidents. The methodology incorporates data collection from network traffic, endpoint activities, authentication events, application logs, cloud workloads, and security alerts, followed by preprocessing, feature engineering, model training, validation, and continuous monitoring. Predictive models are employed to estimate threat likelihood and prioritize risks according to behavioral and contextual indicators. The framework further supports adaptive security responses through automated alert generation, risk scoring, and coordinated mitigation mechanisms. By combining predictive intelligence with continuous monitoring and enterprise-wide security analytics, the proposed framework aims to reduce detection delays, improve security visibility, minimize false positives, and enhance organizational cyber resilience. The research provides a scalable foundation for intelligent cybersecurity operations across modern cloud-connected digital enterprises.