Hybrid Deep Learning Model Based Advanced AI-Driven Identity and Access Management System for Enhanced Security and Efficiency

Identity and access management (IAM) systems are essential for securing enterprise environments by ensuring that only authorized users can access critical resources. However, traditional IAM systems often fail to address the complexity of evolving cyber threats. This paper introduces an AI-driven IAM system that enhances security protocols through real-time anomaly detection. By leveraging a hybrid architecture consisting of convolutional neural networks (CNN) and long short-term memory (LSTM) layers, the system provides real-time analysis of user behavior to detect identity-related anomalies. The data was collected from real-world environments using a.NET worker service and preprocessing involved user-specific normalization techniques. The proposed model achieved test accuracy of $85.44 \%$, precision of $87.95 \%$, recall of $85.44 \%$, and area under curve (AUC) score of 0.8578. These results demonstrate the model’s ability to provide scalable and adaptive solutions for modern IAM challenges.