AI-DRIVEN THREAT INTELLIGENCE PLATFORM FOR PROACTIVE CYBERATTACK PREDICTION IN ENTERPRISE NETWORKS

Abstract

The increasing sophistication and frequency of cyberattacks in enterprise environments demand security solutions capable of detecting threats before they materialize. Traditional signature-based and rule-driven systems often fail to address emerging, zero-day, and polymorphic attacks due to their limited adaptability and reactive nature. To overcome these limitations, this paper proposes an AIDriven Threat Intelligence Platform that integrates real-time network telemetry, behavioral analytics, and advanced machine learning models to enable proactive cyberattack prediction. The platform leverages deep learning architectures, graph-based threat correlation, and ensemble anomaly detection techniques to identify early indicators of compromise across heterogeneous enterprise systems. A multi-layer intelligence pipeline fuses threat feeds, endpoint signals, user behavior analytics, and historical incident patterns to generate high-confidence predictive alerts. Experimental evaluation using real-world datasets demonstrates significant improvements in detection accuracy, early warning capability, and reduction in false positives compared to conventional solutions. The proposed platform enhances enterprise resilience by providing anticipatory defense mechanisms that empower organizations to mitigate attacks before they cause operational disruption or data loss.