The increasing threats from unauthorized access, insider threats, and data breaches have made the need for research in enhancing security within human resource management (HRM) systems critical for organizations. In that regard, the importance of incorporating advanced security measures in such systems arises since they contain sensitive information regarding employees. This research delves into the use of ML models to solve the above challenges through the development of an automated real-time predictive and preventive security breach system. The present study makes use of four different ML techniques: random forest (RF), artificial neural networks (ANNs), support vector machines (SVMs), and gated recurrent units (GRUs), on employee access logs, biometric authentication data, and the security-related event logs. The models were exposed to a mix of publicly accessible datasets made available by Kaggle, along with real-time data coming in from an institution’s HRM system. After preprocessing the data, the performance of the models was evaluated with the help of metrics such as accuracy, precision, recall, and F1-score, and GRU turned out to be the winner with an accuracy of 95.89%. The SVM and RF models also delivered good accuracies at 93.45% and 91.20%, respectively, with ANN giving a slightly lower accuracy at 89.76%. The outcomes show that there is tremendous potential for improving HRM system security from machine learning models, particularly GRU, in spotting suspicious login attempts as well as patterns of unauthorized access. The original idea behind this research is to contribute to the development of real-time, proactive security solutions that can be integrated into HRM systems to safeguard sensitive organizational data and improve overall security posture.