Explainable Autonomous Intelligence Frameworks for Next-Generation Cloud and Cyber Defense Systems
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Abstract
The rapid growth of cloud computing, Internet of Things (IoT), edge infrastructures, and artificial intelligence-driven applications has significantly increased the complexity of cybersecurity threats in modern digital ecosystems. Traditional security frameworks are often incapable of responding to sophisticated cyberattacks in real time due to limited adaptability, centralized monitoring constraints, and insufficient transparency in decision-making. Explainable Autonomous Intelligence (EAI) frameworks have emerged as a promising solution for strengthening next-generation cloud and cyber defense systems by integrating autonomous machine learning models with explainable artificial intelligence mechanisms. These frameworks enable intelligent threat detection, automated response, anomaly prediction, and continuous risk assessment while ensuring interpretability and trustworthiness in security operations. Explainability improves transparency by allowing security analysts and organizations to understand the reasoning behind autonomous decisions, thereby supporting compliance, accountability, and ethical governance. Furthermore, EAI frameworks enhance resilience against zero-day attacks, insider threats, ransomware, distributed denial-of-service attacks, and advanced persistent threats. This study explores the architecture, principles, methodologies, benefits, and limitations of explainable autonomous intelligence frameworks in cloud and cybersecurity environments. The research also highlights the role of explainability in improving operational efficiency, decision accuracy, trust management, and adaptive cyber defense capabilities for future intelligent cloud infrastructures.
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