Securing Modern Web Applications Using AI-Driven Static and Dynamic Analysis Techniques

The application layer in the modern web, developed for various purposes such as banking, social networking, etc., is widely exposed to cyber threats due to loopholes in application architecture. To address these issues, it has been found that the incorporation of AI in security analysis has been quite effective. In this paper, the author scrutinizes the manner in which static and dynamic analysis methods propelled by artificial intelligence can strengthen the security of the current web-intensified applications. Here, we see the very essence of modern web applications and identify the significant increase in the number and depth of threats. We then provide a comparative analysis of traditional and AI methods for detecting vulnerabilities. What pertains to static analysis that analyzes code without executing it is discussed regarding applying machine learning classifiers and code understanding based on NLP. On the other hand, dynamic analysis that involves determining the behavior of an application in operation can rely on reinforcement learning and anomaly detection. In this paper, we propose a framework that incorporates both approaches which is well demonstrated through an actual e-commerce environment. Given outcomes suggest a gain in the number of birds detected, minimized false alarms, and quicker response time. It also covers implementation issues such as the lack of datasets and generalizing and incorporating the model into the DevSecOps pipeline. In conclusion, incorporating AI-based analysis provides an active and elastic approach to safeguard web applications against existing and arising hazards