AI-POWERED RISK-BASED ACCESS CONTROL: ADVANCED SECURITY FRAMEWORK FOR MODERN SYSTEMS
Keywords:
AI-Powered Access Control, Risk-Based Security, Quantum-AI Integration, Dynamic Policy Adaptation, Machine Learning SecurityAbstract
This article presents a comprehensive analysis of AI-Powered Risk-Based Access Control (RBAC) systems as an advanced security framework for modern organizations. The article examines the evolution from traditional RBAC to AI-enhanced systems, highlighting significant improvements in threat detection, policy adaptation, and operational efficiency. The article explores the integration of machine learning algorithms, quantum computing capabilities, and dynamic risk assessment methodologies across various implementation scenarios. Through detailed analysis of system architecture, performance metrics, and implementation challenges, the article demonstrates how AI-powered RBAC systems effectively address the complex security requirements of contemporary digital environments, particularly in multi-cloud and IoT deployments. The article also investigates future developments in quantum-AI integration and their potential impact on next-generation security frameworks.
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