Enterprise Cloud Security Enhancement Through AI-Powered Threat Intelligence and Predictive Attack Detection
DOI:
https://doi.org/10.21590/ijtmh.10.04.52Keywords:
Artificial Intelligence,, Enterprise Cloud Security,, Threat Intelligence,, Predictive Attack Detection,, Machine Learning,, Deep Learning,, Cybersecurity,Abstract
The rapid adoption of cloud computing has transformed enterprise IT infrastructure by providing scalable
resources, flexible data storage, and cost-effective computing capabilities. However, increasing
dependence on cloud environments has introduced significant cybersecurity challenges, including
unauthorized access, malware infections, distributed denial-of-service (DDoS) attacks, insider threats,
ransomware, and advanced persistent threats. Traditional security mechanisms often struggle to identify
sophisticated attacks because they primarily depend on predefined rules, signatures, and reactive
monitoring techniques. This research proposes an AI-powered threat intelligence and predictive attack
detection framework to enhance enterprise cloud security. The proposed approach integrates machine
learning, deep learning, behavioral analytics, and real-time threat intelligence to identify suspicious
activities and predict potential cyberattacks before they cause significant damage. The methodology
focuses on collecting cloud security logs, preprocessing network traffic information, extracting relevant
security features, and developing predictive models for threat classification. Furthermore, automated
threat monitoring and intelligent incident response mechanisms are considered to improve detection
accuracy and reduce response time. The proposed framework aims to strengthen cloud infrastructure
protection, improve organizational risk management, and support proactive cybersecurity decision-
making. The research also examines the importance of scalability, data privacy, model reliability, and
continuous learning in enterprise cloud security environments.
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