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Predictive Cybersecurity, Threat Intelligence, Artificial Intelligence, Zero-Day Vulnerability, Automated Defense.

Traditional, reactive security postures are no longer enough to defend against the increasingly sophisticated cyber threats we face – particularly zero-day attacks. This article presents an AIBased Threat Intelligence Platform that provides predictive posture for Cyber Defense. The system goes beyond signature-based detection by collecting and analyzing comprehensive, multi-modal datasets to predict and neutralize threats before they materialize. In this paper, we propose a new approach which combines internal network behavior analysis with external unstructured data intelligence. The performance of the platform was demonstrated on a dedicated curated dataset, ZDA-NetTraffic-459, including 459 examples representing both normal traffic and artificial zero-day exploit signatures. The prototype was Python-based and utilized state-ofthe- art computational libraries for data crunching and pattern recognition, using a graph database to model multi-dimensional interactions between threat-actors. We demonstrate that our AIbased system can maintain the same 94.5% general accuracy in predicting of novel threats without the need for reannotation, therefore effectively reducing detection time from days to seconds while keeping a false positive rate below 2.1%. This paper shows that there is a workable transition from reactive incident response to predictive, proactive defense that can counter previously unknown vulnerabilities.