Designing Machine Learning Enabled Cloud Computing Frameworks for Intelligent Enterprise Security and API Modernization .
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Abstract
Cloud computing has become the backbone of modern enterprise digital transformation, enabling organizations to achieve greater scalability, flexibility, and operational efficiency. As businesses increasingly rely on cloud-native applications and interconnected services through Application Programming Interfaces (APIs), the complexity of securing enterprise environments has grown substantially. Traditional security mechanisms often struggle to detect sophisticated cyber threats, manage dynamic workloads, and protect rapidly expanding API ecosystems. Machine learning (ML) offers a transformative approach by enabling intelligent threat detection, predictive analytics, anomaly identification, and automated security responses. Simultaneously, API modernization supports seamless integration between legacy systems and cloud-native architectures, enhancing interoperability, performance, and business agility. This study examines the design of machine learning-enabled cloud computing frameworks that integrate intelligent enterprise security with API modernization strategies. The proposed conceptual framework emphasizes automated threat intelligence, adaptive authentication, behavioral analytics, continuous monitoring, and intelligent API governance to strengthen cloud security while improving operational efficiency. The research also explores implementation challenges, including data privacy, model scalability, algorithm transparency, integration complexity, and regulatory compliance. The findings suggest that combining machine learning with cloud computing and modern API architectures creates resilient, secure, and intelligent enterprise ecosystems capable of responding proactively to evolving cybersecurity threats while supporting sustainable digital innovation and enterprise transformation.
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