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@article{186771,
        author = {Paras Shigvan and Proff. Sheetal Shevkari and Vishal Auti and Gaurav Kumar},
        title = {Detecting and Mitigating Insider Threats with Artificial Intelligence},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {4812-4816},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186771},
        abstract = {Insider threat-threatening or irresponsible conduct of authorized users is an old and continuously developing danger to organizations. Traditional perimeter-based security tools have limited ability to identify this type of attack because insiders have valid access and understanding of the system. The latest achievements in artificial intelligence (AI) and machine learning (ML), behavioral analytics, anomaly detection, natural language processing (NLP), graph-based models, and federated learning provide the new opportunities to detect, prioritize, and reduce insider risks. The present paper outlines AI- based insider threat detection techniques, synopticizes the key issues (data scarcity, privacy, false positives, concept drift, and interpretability), integrates an insider threat detection pipeline, and finally, evaluates the performance criteria and future opportunities. Suggestions are also given on the need to combine technical controls with the organizational policies and human supervision.},
        keywords = {Insider Threat Detection, Artificial Intelligence, Machine Learning, Behavioral Analytics, Anomaly Detection, Natural Language Processing, Graph Neural Networks, Federated Learning, Privacy-Preserving AI, Explainable AI.},
        month = {November},
        }