Effective human resource management requires continuous monitoring of workforce dynamics, including role transitions, promotions, and structural changes within an organization. This paper presents a solution based on recurrent neural networks (RNN), utilizing LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) architectures to analyze sequential data derived from employee interactions in a large organizational environment. The research was conducted using a text-based dataset of approximately 184 GB, encompassing various communication formats from emails and meeting transcripts to team discussions while incorporating organizational hierarchy context. The proposed model detects significant personnel events, such as changes in supervisors, promotions, or positional shifts. The analysis considers 16 features describing relationships between employees and their organizational surroundings. The use of LSTM and GRU architectures enabled the capture of complex temporal dependencies and accurate classification of career-related behavioral patterns. Designed for near real-time operation, the system supports the rapid identification of potential anomalies and assists managerial decision-making. This approach may be applied in both private and public sector institutions, wherever workforce management and information security are of strategic importance.