Model for Strengthening Network Security Through Intelligent Policy Automation and Compliance Systems

This is the authors' abstract. We don't add key points for this paper.

The rapid expansion of digital infrastructures and interconnected systems has intensified the demand for robust, adaptive, and intelligent network security mechanisms. Traditional security management approaches characterized by static rule sets, manual policy updates, and reactive incident responses are increasingly inadequate for addressing the complexity and dynamism of modern cyber threats. This study proposes a Model for Strengthening Network Security Through Intelligent Policy Automation and Compliance Systems, which integrates Artificial Intelligence (AI), Machine Learning (ML), and Software-Defined Networking (SDN) to create a proactive, self-adaptive security framework. The model automates policy enforcement, compliance verification, and threat mitigation through data-driven analytics and real-time decision-making. The proposed architecture employs a multi-layer design, consisting of a policy management layer for automated rule generation, an intelligence layer for anomaly detection and behavioral analysis, and a control layer that leverages SDN controllers for dynamic network configuration. By integrating ML algorithms with continuous monitoring systems, the framework enables context-aware security adaptation, ensuring that network defenses evolve in tandem with changing traffic patterns and threat landscapes. Furthermore, compliance with regulatory standards such as GDPR, ISO/IEC 27001, and NIST frameworks is maintained through automated auditing and policy validation mechanisms, reducing human error and ensuring consistent governance. Simulation and evaluation of the model demonstrate significant improvements in threat detection accuracy, response speed, and policy consistency compared to traditional rule-based systems. The framework’s intelligent automation minimizes manual intervention while enhancing transparency and accountability through explainable AI (XAI) mechanisms. This research contributes to the advancement of autonomous cybersecurity ecosystems, promoting resilience, scalability, and trust in digital enterprises. Future developments will explore federated learning, blockchain-based policy verification, and integration with quantum-safe security protocols to further strengthen adaptive, compliant, and intelligent network security management.