PROACTIVE SECURITY COMPLIANCE: LEVERAGING PREDICTIVE ANALYTICS IN WEB APPLICATIONS

Abstract

This paper explores the application of predictive analytics using ML to proactively manage security compliance in web applications. A dataset comprising historical data on security incidents and compliance status was used to train and evaluate several ML models, including Decision Trees, Random Forest, SVM, and Gradient Boosting. The Gradient Boosting model exhibited the highest predictive accuracy, achieving 90% accuracy and AUC score of 0.94. Feature importance analysis identified historical vulnerabilities as the most significant predictor of compliance issues, followed by user role complexity and application type.

The results demonstrate the effectiveness of using predictive analytics to anticipate potential compliance violations, allowing organizations to take proactive measures and mitigate risks. The study highlights the practical applications of these models in real-world scenarios, where proactive interventions can prevent security breaches and ensure regulatory adherence. Additionally, the paper discusses the implications of these findings for web application security strategies and suggests future research directions, including the integration of more advanced ML techniques and real-time monitoring systems.