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AI-driven Security Enhancements for Web Applications

Authors:

Mariappan Ayyarrappan
Principle Software Engineer, Tracy, CA, USA
Email: mariappan.cs@gmail.com

Abstract: As the sophistication of cyber threats escalates, traditional security measures—firewalls, basic intrusion detection systems, and static rule checks—often struggle to keep pace. Recent advancements in artificial intelligence (AI) provide novel opportunities to fortify web application security. This paper discusses how AI-driven methods, such as machine learning–based anomaly detection, natural language processing (NLP) for threat intelligence, and predictive analytics, can enhance protection against a broad range of attacks (e.g., SQL injection, Cross-Site Scripting). We include diagrams and charts to illustrate conceptual models of AI-based security flows, highlight best practices for data ingestion and feature engineering, and address challenges like false positives and model drift. By adopting AI-driven security enhancements, organizations can proactively respond to evolving threats, reducing exposure and fortifying their web applications.

Keywords: AI Security, Web Applications, Intrusion Detection, Machine Learning, Threat Intelligence, Cyber Attacks