Insider Threats and Countermeasures Based on AI Lie Detection

Konstantinos Kalodanis, Panagiotis Rizomiliotis, Charalampos Papapavlou, Apostolos Skrekas, Stavros Papadimas, Dimosthenis Anagnostopoulos

2025

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 methodology 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.

Paper Citation

in Harvard Style

Kalodanis K., Rizomiliotis P., Papapavlou C., Skrekas A., Papadimas S. and Anagnostopoulos D. (2025). Insider Threats and Countermeasures Based on AI Lie Detection. In Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-760-3, SciTePress, pages 321-328. DOI: 10.5220/0013454900003979

in Bibtex Style

@conference{secrypt25,
author={Konstantinos Kalodanis and Panagiotis Rizomiliotis and Charalampos Papapavlou and Apostolos Skrekas and Stavros Papadimas and Dimosthenis Anagnostopoulos},
title={Insider Threats and Countermeasures Based on AI Lie Detection},
booktitle={Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2025},
pages={321-328},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013454900003979},
isbn={978-989-758-760-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Insider Threats and Countermeasures Based on AI Lie Detection
SN - 978-989-758-760-3
AU - Kalodanis K.
AU - Rizomiliotis P.
AU - Papapavlou C.
AU - Skrekas A.
AU - Papadimas S.
AU - Anagnostopoulos D.
PY - 2025
SP - 321
EP - 328
DO - 10.5220/0013454900003979
PB - SciTePress