Integrating Zero Trust Principles into IAM for Enhanced Cloud Security

This paper investigates the integration of Zero Trust principles into Identity and Access Management (IAM) frameworks to strengthen security in multi-cloud and hybrid cloud environments. Unlike traditional perimeter-based defenses, the Zero Trust model enforces rigorous verification for every access request, ensuring that no entity, internal or external, is implicitly trusted. Our methodology incorporates dynamic trust scoring, continuous identity verification, adaptive privilege adjustments, and real-time monitoring to secure cloud infrastructures against evolving threats. By employing a multi-layered approach, including critical components like Advanced Encryption Standard (AES) for data security, contextual behavior analysis, and anomaly detection powered by machine learning, our Zero Trust IAM framework provides a scalable and proactive security solution. Experimental results demonstrate notable enhancements in security, with unauthorized access reduced by 30% and improved threat detection response times across various cloud services. The adaptive trust scoring effectively limits access based on real-time behavioral, contextual, and device-based factors, reducing risks from lateral movement and insider threats. The results further indicate that Zero Trust improves compliance management by enforcing strict access controls and continuous monitoring, which aligns well with regulatory standards. We discuss challenges in implementing Zero Trust in complex cloud environments and provide best practices for adoption. This work underscores Zero Trust as a robust, scalable IAM strategy, essential for a secure and resilient cloud ecosystem.