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The accelerated integration of artificial intelligence systems and its potential to expand the vulnerability of the critical infrastructure

Authors :
Luca SAMBUCCI
Elena-Anca PARASCHIV
Source :
Revista Română de Informatică și Automatică, Vol 34, Iss 3, Pp 131-148 (2024)
Publication Year :
2024
Publisher :
ICI Publishing House, 2024.

Abstract

As artificial intelligence (AI) is becoming increasingly integrated into critical infrastructures, it brings about both transformative benefits and unprecedented risks. AI has the potential to revolutionize the efficiency, reliability, and responsiveness of essential services, but it can also offer these benefits along with the vulnerability to a growing array of sophisticated adversarial attacks. This paper explores the evolving landscape of adversarial threats to AI systems, highlighting the potential of nation-state actors to exploit these vulnerabilities for geopolitical gains. A range of adversarial techniques is examined, including dataset poisoning, model stealing, and privacy inference attacks, and their potential impact on sectors such as energy, transportation, healthcare, and water management is assessed. The consequences of successful attacks are substantial, encompassing economic disruption, public safety risks, national security implications, and the erosion of public trust. Given the escalating sophistication of these threats, this paper proposes a comprehensive security framework that includes robust incident response protocols, specialized training, the development of a collaborative ecosystem, and the continuous evaluation of AI systems. The findings of this study 11 underscore the critical need for a proactive approach to AI security in order to safeguard the future of critical infrastructures in an increasingly AI-driven world.

Details

Language :
English, Romanian; Moldavian; Moldovan
ISSN :
12201758 and 18414303
Volume :
34
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Revista Română de Informatică și Automatică
Publication Type :
Academic Journal
Accession number :
edsdoj.1b99216caf9b453bb93913aff59a5a8e
Document Type :
article
Full Text :
https://doi.org/10.33436/v34i3y202410