This paper presents a novel user access security system for cloud applications, designed to address the growing security challenges posed by the widespread adoption of cloud applications, especially in the context of remote work. The system leverages user activity tracking, combined with data from user, device, and contextual identity, along with Identity Provider (IdP) information, Natural Language Processing (NLP), and Machine Learning (ML) algorithms. It builds user baselines and tracks deviations to identify and proactively prevent potential threats in real-time, working in conjunction with Security Orchestration, Automation, and Response (SOAR) tools. Deviations from the baselines, which may indicate compromised accounts or malicious intent, trigger proactive interventions. This approach offers organizations superior visibility and control over their cloud applications, enabling proactive and real-time threat detection and data breach prevention. The system is designed to collect and analyze data from various sources, including user, device, application, identity provider, proxy, firewall, and cloud application vendor logs. It uses a decentralized intelligence approach with a highly scalable microservice architecture, allowing both human and artificial intelligence to coexist, with the ultimate control lying with humans (admin). The system requires a learning period of 90 days to ensure accurate detections based on historical data and to protect them for future baseline predictions. The system's unique architecture enables it to provide real-time action and notifications to admins and suspicious users, while also allowing for justification workflows. The system is tested against various scenarios, including compromised accounts, privilege misuse, data exfiltration, and sensitive data breaches. It effectively identifies and mitigates these threats by analyzing user behavior, application access patterns, and data activity deviations. The system's ability to stack-rank entities and provide risk scores allows administrators to prioritize remediation efforts and allocate resources more efficiently, thereby strengthening the organization's overall security posture. The proposed system is a uniquely intelligent system that redefines traditional thinking with decentralized intelligence across the system. It combines advanced technologies including machine learning models, large language models, and natural language processing to provide a comprehensive approach to identifying and addressing security risks. By leveraging these methodologies, the system enhances the accuracy and efficiency of threat detection, enabling organizations to respond swiftly and effectively to potential security incidents. The research highlights the effectiveness of an integrated approach to cloud data security, combining advanced technologies with proactive monitoring and response mechanisms. By adopting such strategies, organizations can fortify their defenses against emerging threats and mitigate the risks associated with sensitive data access and exfiltration, thus safeguarding the integrity and confidentiality of their valuable assets.This paper presents a novel user access security system for cloud applications, designed to address the growing security challenges posed by the widespread adoption of cloud applications, especially in the context of remote work. The system leverages user activity tracking, combined with data from user, device, and contextual identity, along with Identity Provider (IdP) information, Natural Language Processing (NLP), and Machine Learning (ML) algorithms. It builds user baselines and tracks deviations to identify and proactively prevent potential threats in real-time, working in conjunction with Security Orchestration, Automation, and Response (SOAR) tools. Deviations from the baselines, which may indicate compromised accounts or malicious intent, trigger proactive interventions. This approach offers organizations superior visibility and control over their cloud applications, enabling proactive and real-time threat detection and data breach prevention. The system is designed to collect and analyze data from various sources, including user, device, application, identity provider, proxy, firewall, and cloud application vendor logs. It uses a decentralized intelligence approach with a highly scalable microservice architecture, allowing both human and artificial intelligence to coexist, with the ultimate control lying with humans (admin). The system requires a learning period of 90 days to ensure accurate detections based on historical data and to protect them for future baseline predictions. The system's unique architecture enables it to provide real-time action and notifications to admins and suspicious users, while also allowing for justification workflows. The system is tested against various scenarios, including compromised accounts, privilege misuse, data exfiltration, and sensitive data breaches. It effectively identifies and mitigates these threats by analyzing user behavior, application access patterns, and data activity deviations. The system's ability to stack-rank entities and provide risk scores allows administrators to prioritize remediation efforts and allocate resources more efficiently, thereby strengthening the organization's overall security posture. The proposed system is a uniquely intelligent system that redefines traditional thinking with decentralized intelligence across the system. It combines advanced technologies including machine learning models, large language models, and natural language processing to provide a comprehensive approach to identifying and addressing security risks. By leveraging these methodologies, the system enhances the accuracy and efficiency of threat detection, enabling organizations to respond swiftly and effectively to potential security incidents. The research highlights the effectiveness of an integrated approach to cloud data security, combining advanced technologies with proactive monitoring and response mechanisms. By adopting such strategies, organizations can fortify their defenses against emerging threats and mitigate the risks associated with sensitive data access and exfiltration, thus safeguarding the integrity and confidentiality of their valuable assets.