A Behavior-Aware Identity and Access Monitoring Framework for Securing Outsourced Data in Cloud Environments

Data outsourcing presents major challenges in the monitoring of unauthorized identities and the access behavior in the modern cloud computing. To resolve this, this study proposes a holistic model in the trustworthy detection of anomalies in the environment of cloud replication, whilst having twofold aims of improving data safety as well as robust surveillance. The suggested solution gathers the information of various sources in a systematic manner, such as user profiles, access logs, and cloud records. By using modern technologies like AWS CloudTrail and Apache Kafka, the data is preprocessed, cleansed, normalized and features extracted. The feature engineering is critical in shaping the user behavior patterns with the consideration of factors like frequency of logins, access patterns of files and system command. In anomaly detection, the framework uses a variety of strategies, such as unsupervised learning algorithms (k-means, isolation forest), statistical approaches, and neural networks (autoencoders, RNNs). Proactive security is provided by real-time notifications, automated reactions and interactive dashboards, and smoothly integrated into Security information and event management frameworks (SIEM). Extensive tests prove the effectiveness of the framework in identifying illegal access and the provision of security teams with practical information. The study provides a sophisticated solution to enhance the security of the cloud and solve identity theft and access control in outsourced data environments.