AI-driven automation for network configuration and compliance: Transforming enterprise security posture

Thati, Suresh Reddy (2025) AI-driven automation for network configuration and compliance: Transforming enterprise security posture. World Journal of Advanced Research and Reviews, 26 (2). pp. 1216-1223. ISSN 2581-9615

Artificial intelligence is revolutionizing network configuration management by addressing the limitations of traditional approaches in increasingly complex digital environments. This transformation enables organizations to shift from reactive to proactive management of network infrastructures through continuous monitoring, automated remediation, and intelligent optimization. The integration of machine learning, natural language processing, reinforcement learning, and deep learning technologies allows for pattern recognition in configurations, translation of business requirements into technical implementations, performance optimization, and anomaly detection that far exceeds human capabilities. These advancements facilitate real-time compliance verification and enforcement, dramatically reducing the security vulnerability window while improving operational efficiency. Across telecommunications, healthcare, and financial services sectors, organizations implementing AI-driven configuration management have achieved significant improvements in security posture, regulatory compliance, network reliability, and cost efficiency. The consistent results across diverse industries underscore the broad applicability of these technologies regardless of specific requirements or regulatory frameworks, representing a fundamental shift in how enterprise networks are configured, monitored, and secured.