Multi-Modal AI System for Real Time Insider Threat Recognition

Insider threats pose a major cybersecurity risk as they originate from authorized users who misuse access privileges, causing data breaches and financial losses. Traditional rule-based security systems often fail to detect such threats due to evolving user behaviour patterns. This research proposes a deep learning–based insider threat detection system using Long Short-Term Memory (LSTM) networks and Autoencoders. The LSTM model captures temporal patterns in user activity, while the Autoencoder learns normal behaviour and detects anomalies through reconstruction errors. The hybrid approach improves detection accuracy, reduces false positives, and supports effective monitoring of organizational activities. Although the system shows better performance compared to traditional methods, challenges related to computational complexity and real-time deployment remain areas for future work.