AUTOMATING FIREWALL POLICY MANAGEMENT USING AI AND MICROSERVICES FOR ENHANCED NETWORK SECURITY

Abstract

This article presents an innovative approach to automating firewall policy management through the integration of artificial intelligence (AI) and microservices architecture. The proposed article framework addresses critical challenges in traditional firewall management, including rule redundancy, configuration inconsistencies, and human error. By leveraging natural language processing for policy review and machine learning for anomaly detection, the system enhances the accuracy and efficiency of policy implementation. The microservices architecture provides a scalable and flexible foundation, allowing for real-time threat intelligence integration and automated policy updates. Case studies demonstrate significant improvements in policy optimization, threat response times, and operational efficiency in large enterprise environments. The article also explores best practices for deployment, focusing on scalability, regulatory compliance, and risk mitigation strategies. Finally, it examines future directions and emerging trends, highlighting the potential for broader applications in cybersecurity. This comprehensive article approach offers a promising solution for organizations seeking to enhance their network security posture in the face of increasingly complex and dynamic threat landscapes.