The rapid expansion of web technologies has increased the risk of cyber attacks targeting web applications. Traditional rule based security mechanisms struggle to identify evolving threats such as SQL injection, cross site scripting, and distributed denial of service attacks. Artificial Intelligence (AI) and Machine Learning (ML) offer the ability to learn patterns from network traffic and identify malicious activity. This paper proposes an AI based web security system designed to monitor web traffic, analyze behavior, and detect cyber attacks in real time. The system integrates data preprocessing, feature extraction, and a deep learning classification model to distinguish between normal and malicious traffic. Experiments performed using a benchmark intrusion detection dataset demonstrate that AI driven detection methods significantly improve accuracy and adaptability compared to traditional approaches. The results indicate that intelligent security frameworks can enhance web application protection and reduce response time against cyber threats.