Authors:
Konstantinos Kalodanis
1
;
Panagiotis Rizomiliotis
1
;
Charalampos Papapavlou
2
;
Apostolos Skrekas
3
;
Stavros Papadimas
3
and
Dimosthenis Anagnostopoulos
1
Affiliations:
1
Department of Informatics & Telematics, Harokopio University of Athens, Kallithea, Athens, Greece
;
2
Department of Electrical & Computer Engineering, University of Patras, Rio, Patras, Greece
;
3
Department of Management Science & Technology, Athens University of Economics & Business, Athens, Greece
Keyword(s):
AI, Lie Detection, Insider Threat, EU AI Act.
Abstract:
Insider threats continue to pose some of the most significant security risks within organizations, as malicious insiders have privileged access to sensitive or even classified data and systems. This paper explores an emerging approach that applies Artificial Intelligence (AI)–based lie detection techniques to mitigate insider threats. We investigate state-of-the-art AI methods adapted from Natural Language Processing (NLP), physiological signal analysis, and behavioral analytics to detect deceptive behavior. Our findings suggest that the fusion of multiple data streams, combined with advanced AI classifiers such as transformer-based models and Graph Neural Networks (GNN), leads to enhanced lie detection accuracy. Such systems must be designed in accordance with EU AI Act, which imposes requirements on transparency, risk management, and compliance for high-risk AI systems. Experimental evaluations on both synthesized and real-world insider threat datasets indicate that the proposed me
thodology achieves a performance improvement of up to 15–20% over conventional rule-based solutions. The paper concludes by exploring deployment strategies, limitations, and future research directions to ensure that AI-based lie detection can effectively and ethically bolster insider threat defences.
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