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Author: Thomas Boufikos

  • TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”

    AI-Enhanced Cybersecurity in Critical Infrastructures: A TRITON Framework and Review

    • Valeri Mladenov
    • Gergana Vacheva
    • Sophia Karagiorgou
    • Mariza Konidi
    • Evangelos Kafantaris
    • Ioannis Pastellas
    • Theodora Anastasiou
    • Angie Carillo
    • Angeliki Vlachostergiou
    • Alberto Mozo
    • Esteban Martínez-Hoces
    • Thomas Boufikos
    • Panagiotis Radoglou- Grammatikis
    • Luca Demetrio
    Industry 4.0, Vol. 11 (2026), Issue 1, pg(s) 3-7
    • Abstract
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    •  Article PDF

    Artificial intelligence (AI) is reshaping the field of penetration testing by enabling faster, more adaptive simulations of cyber threats. This paper explores how AI can be ethically integrated into penetration testing processes, focusing on the European Defence Fundbacked TRITON project. TRITON proposes a comprehensive AI-driven framework for testing the security of military and critical infrastructure systems, combining technologies like machine learning, generative models, and reinforcement learning. The TRITON project reviews recent advancements in AI-supported pentesting and discusses how these tools can improve vulnerability detection, attack simulations, and threat modeling. Alongside the technical discussion, which is examined in the TRITON project, ethical concerns—including transparency, human oversight, and dual-use risks—are of the essence and must be addressed to ensure responsible use. Comparisons with other EU cybersecurity initiatives, such as AI4CYBER and CyberSecDome, highlight TRITON’s unique contributions and focus areas. Ultimately, it argues that AI-enhanced penetration testing can significantly strengthen cybersecurity defenses when implemented with appropriate safeguards and ethical oversight.

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