AI-Powered Enterprise Cloud Frameworks for Secure Data Integration and Predictive Threat Intelligence
DOI:
https://doi.org/10.15662/IJEETR.2023.0505013Keywords:
Artificial intelligence, enterprise cloud computing, secure data integration, predictive threat intelligence, cybersecurity, machine learning, cloud security, data analytics, threat detection, intelligent automationAbstract
Artificial intelligence (AI)-powered enterprise cloud frameworks have emerged as a transformative approach for addressing the growing complexity of secure data integration and predictive threat intelligence in modern digital ecosystems. Organizations increasingly rely on cloud infrastructures to manage distributed data sources, business applications, and interconnected services; however, this expansion creates significant cybersecurity challenges, including unauthorized access, data breaches, and sophisticated cyberattacks. AI-driven cloud frameworks combine machine learning, automation, and advanced analytics to enhance data protection, improve interoperability, and predict emerging security threats. These frameworks enable real-time monitoring, anomaly detection, intelligent decision-making, and proactive risk mitigation by analyzing large-scale data patterns across enterprise environments. This research examines the role of AI-powered enterprise cloud frameworks in establishing secure data integration architectures and strengthening predictive threat intelligence capabilities. The study explores existing technological approaches, methodological considerations, and future directions for developing resilient, adaptive, and intelligent cloud security ecosystems
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