Posted: Nov 01, 2023
The banking sector faces unprecedented challenges in network security policy management, with traditional approaches proving increasingly inadequate against sophisticated cyber threats. Current security policy frameworks in financial institutions often rely on static rule-based systems that lack the adaptability required for modern distributed banking environments. These conventional methods struggle to balance security requirements with operational efficiency, particularly as banking services expand across digital platforms and cloud infrastructures. The limitations of existing approaches become particularly evident in their inability to dynamically respond to emerging threats while maintaining compliance with evolving regulatory standards. This research addresses these challenges through the development of an innovative security policy framework that integrates cutting-edge computational techniques from diverse disciplines. The proposed methodology represents a significant departure from traditional banking security approaches by incorporating quantum-resistant cryptographic principles, federated learning architectures, and bio-inspired optimization algorithms. These elements work in concert to create a dynamic, self-optimizing security policy system capable of adapting to the complex threat landscape facing modern financial institutions. The novelty of our approach lies in its holistic integration of multiple advanced techniques into a cohesive framework specifically designed for banking environments. Unlike previous work that has focused on individual aspects of security policy management, our research addresses the entire policy lifecycle from development through implementation and continuous optimization. This comprehensive approach enables financial institutions to maintain robust security postures while accommodating the operational demands of modern banking services.
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