The security landscape is changing constantly, traditional defensive measures like WAFs (Web Application Firewalls) and signature-based threat detection machine systems are not able to tackle sophisticated attacks. In this study we present a central AI model processed for real-time detection, prevention and autonomous patching of vulnerabilities without human interference. Utilizing Machine Learning (ML), Large Language Models (LLMs), and Reinforcement Learning (RL), the outlined model continuously monitors running applications for security vulnerabilities and deploys real-time remediation strategies. Our model does not rely on static rules as most traditional security tools do, but rather continuously adapts to new threats by observing patterns in the attack vectors. This approach significantly reduces the window of exploitation for attackers and minimizes system downtime. By integrating NLP-based vulnerability analysis, heuristic threat detection, and self-improving federated learning, our AI model redefines automated cybersecurity defenses.

AI Model, ML, LLM, NLP, Reinforcement Learning, Cybersecurity, Real-Time Threat Detection, Self-Patching, Heuristic Security, Federated Learning.

Cybersecurity threats continue to evolve, becoming more sophisticated and capable of bypassing traditional security measures that rely on static signatures and human intervention. While AI-driven solutions have enhanced threat detection and response, they remain fragmented, reactive, and prone to high false positive rates. Many existing models still require manual oversight, slowing down the mitigation process and leaving critical systems vulnerable. Our research introduces a self-evolving AI cybersecurity model that overcomes these limitations by integrating reinforcement learning, federated learning, symbolic AI, NLP-based vulnerability detection, and kernel-level monitoring into a unified defence mechanism. This approach not only improves threat detection accuracy but also enables real-time autonomous mitigation and self-patching, eliminating the delays associated with manual security updates. By leveraging adaptive learning and real-time decision-making, our model ensures that security defences evolve alongside emerging threats, providing a proactive, predictive, and fully autonomous cybersecurity solution. This research marks a shift in cybersecurity from reactive response to intelligent, automated prevention, redefining how digital infrastructure is secured in an increasingly complex threat landscape.

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