ADAPTIVE AL FRAMEWORK FOR REAL-TIME CYBERSECURITY THREAT PREDICTION AND MITIGATION

The innovation introduces an intelligent, self-learning system designed to detect, predict, and
5 neutralize cybersecurity threats across dynamic digital environments in real time. The
framework leverages a multi-layered adaptive artificial intelligence architecture integrating
deep learning, reinforcement learning, and behavior-based anomaly detection to continuously
analyze vast and heterogeneous data streams from networks, endpoints, and cloud
infrastructures. Unlike conventional static defense mechanisms, the proposed system
10 autonomously evolves its threat models through continuous learning loops, enabling it to
identify novel attack vectors, zero-day exploits, and polymorphic malware with high precision.
A federated learning component ensures decentralized threat intelligence sharing without
compromising data privacy, while an adaptive response engine dynamically prioritizes and
deploys mitigation strategies based on the severity, confidence level, and contextual impact of
15 detected threats.