Adaptive AI-Driven Threat Intelligence System for Real-Time Detection of Zero-Day Cyber Attacks

Zero-day cyber attacks remain one of the most damaging and difficult threats to detect due to the lack of prior knowledge and response signatures. Traditional intrusion detection systems (IDS) and signature-based security mechanisms frequently fail when confronting rapidly evolving attack vectors that exploit unknown vulnerabilities. To address this gap, this study proposes an adaptive AI-driven threat intelligence framework capable of real-time learning from dynamic threat environments. The proposed conceptual model integrates machine learning, anomaly detection, and automated threat intelligence feeds within a continuous feedback loop. In contrast to static models, the system evolves autonomously by updating detection parameters based on new behavioral patterns and contextual information. The results of this theoretical investigation highlight the potential for improved early detection capability, reduced false-positive rates, and accelerated response agility against zero-day attacks. This research contributes to the field by presenting a scalable and resilient architecture that advances proactive cybersecurity defense strategies.