International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Chakra Sheild: AI Powered Insider threat detection system

Author(s) Mr. Manas Tarare, Mr. Ojas Mataghare, Mr. Aaryan Zod, Ms. Mrunal Nathile, Ms. Khushboo Vairagade
Country India
Abstract Insider threats pose severe risks to organizations, as authorized users can misuse legitimate access to cause damage. This paper presents an AI-powered insider threat detection framework that models user behavior through endpoint activity data. The system employs a Bidirectional LSTM Autoencoder to learn normal behavioral patterns and detect anomalies via reconstruction error, enhanced by an Isolation Forest for reducing false positives. A multi-factor threat scoring engine evaluates anomaly intensity, frequency, and recency to assess user risk levels. Experimental results on simulated enterprise data achieved 93.2% accuracy with a 5.1% false positive rate, demonstrating effective behavioral anomaly detection and real-time risk visualization through a Streamlit dashboard.
Keywords Insider threats pose severe risks to organizations, as authorized users can misuse legitimate access to cause damage. This paper presents an AI-powered insider threat detection framework that models user behavior through endpoint activity data. The system employs a Bidirectional LSTM Autoencoder to learn normal behavioral patterns and detect anomalies via reconstruction error, enhanced by an Isolation Forest for reducing false positives. A multi-factor threat scoring engine evaluates anomaly intensity, frequency, and recency to assess user risk levels. Experimental results on simulated enterprise data achieved 93.2% accuracy with a 5.1% false positive rate, demonstrating effective behavioral anomaly detection and real-time risk visualization through a Streamlit dashboard.
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-18
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.60700
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E-ISSN 2582-2160
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