Multi-Modal AI System for Real Time Insider Threat Recognition

Abstract

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.

Introduction

This study proposes a deep learning-based system for detecting insider threats in organizations using a combination of Long Short-Term Memory (LSTM) networks and Autoencoders. Insider threats occur when authorized users, such as employees or contractors, intentionally or accidentally misuse their access, potentially causing data breaches, financial losses, and reputational damage. Traditional security systems often struggle to identify these threats because insiders already have legitimate access to organizational resources.

The proposed system collects user activity logs, including login attempts, file access, downloads, email usage, and network activities. The collected data is cleaned, normalized, and transformed into structured sequences. The LSTM model analyzes user behaviour over time to identify suspicious activity patterns, while the Autoencoder learns normal behaviour and detects anomalies through reconstruction errors. The outputs of both models are combined to generate anomaly scores, and suspicious activities are flagged when they exceed a predefined threshold.

The literature review highlights several AI-based approaches to insider threat detection, including Knowledge Graphs, Large Language Models, Deep Belief Networks, and recurrent neural networks. These approaches can improve detection accuracy but face challenges related to computational complexity, data quality, false positives, and model interpretability.

The experimental results suggest that the combined LSTM and Autoencoder approach can improve insider threat detection compared with traditional rule-based methods, particularly for identifying hidden or gradual behavioural changes. The system also includes a monitoring dashboard that displays alerts, anomaly scores, activity trends, and model performance metrics, helping security administrators identify potential threats and respond promptly.

Conclusion

To enhance insider threat detection, this study proposes a hybrid deep learning framework combining Long Short-Term Memory (LSTM) networks and Autoencoder models for behavioural anomaly detection. The LSTM model effectively captures temporal patterns in user activities, while the Autoencoder identifies deviations from normal behaviour through reconstruction error analysis. The proposed approach improves detection accuracy and reduces false positives compared to traditional rule-based methods. By leveraging sequential learning and anomaly detection together, the system provides an efficient solution for monitoring insider activities in organizational environments. Future work will focus on reducing computational complexity, improving model interpretability, and enabling real-time deployment for large-scale enterprise cybersecurity systems. Our Insider Threat Detection project outperforms traditional models by using a hybrid approach with CNN, LSTM, and Autoencoder, which together improve detection accuracy. CNN captures complex patterns, LSTM models temporal user behavior, and Autoencoder detects anomalies, reducing false positives and negatives. This combination makes the system robust, adaptable, and capable of identifying subtle insider threats proactively, unlike single-model approaches.

References

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Copyright

Copyright © 2026 Mr. Mohit Lade, Ms. Riya Chaturvedi, Ms. Achal Patel, Prof. Sonali Karthik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.