Digital Transformation using Artificial Intelligence and Machine Learning for Secure Enterprises for Secure Enterprise Applications: A Framework using AWS IAM Cloud Security
Keywords:
Artificial Intelligence, Machine Learning, AWS IAM, Cloud Security, Enterprise Applications, Digital Transformation, Anomaly Detection, Access Control, Cybersecurity Framework.Abstract
Complex security issues have been brought about by businesses' quick digital transition, particularly with the use of cloud-based technology. In order to secure enterprise applications, this study offers a thorough framework that combines Amazon Web Services Identity and Access Management (AWS IAM) with Artificial Intelligence (AI) and Machine Learning (ML). Using supervised and unsupervised learning techniques, the framework was created to automate threat response, impose dynamic access control, and identify abnormalities. The Random Forest method outperformed the other models in terms of real-time detection and reaction efficiency and accuracy. The solution demonstrated great scalability, quick response times, and continuous learning capabilities throughout testing using simulated enterprise attack scenarios. The outcomes demonstrate how well AI and ML can improve enterprise cloud security and offer a scalable, flexible, and clever way to counteract online threats. During digital transformation, this approach helps businesses preserve data integrity, compliance, and business continuity.
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