Artificial Intelligence in Ukrainian Law Enforcement

Operational Effectivness and Regulatory Challenges in Countering Hybrid Criminal Offences Against Critical Infrastructure

Resumo

[Purpose] The study examined how Ukrainian law enforcement agencies used artificial intelligence to prevent crimes against critical infrastructure in the context of the escalation of hybrid threats in 2022–2025. The work combined an analysis of the practical use of technologies, threat typologies, and current legislation, which made it possible to assess the effectiveness of AI during real military operations.

[Methodology] A mixed approach was used: surveys, interviews, and case studies of facilities with implemented AI. Quantitative data was processed using statistical methods, qualitative data was processed using thematic analysis. The sample was limited to institutions that already use AI, which allowed us to focus on the real effectiveness of the technologies.

[Findings] AI systems reduced the number of security incidents by approximately 40% and increased operational efficiency by 30%. The technologies successfully detected sabotage attempts, drone reconnaissance, cyber intrusions, and anomalies in SCADA/ICS. At the same time, the study identified gaps in legal regulation, from the lack of transparency requirements for algorithms to unclear rules on the admissibility of data generated by AI.

[Originality/value] The work combines empirical data, legal analysis, and a wartime context. It is one of the first in-depth studies of the use of AI in law enforcement in a hybrid conflict.

[Practical implications] The results confirm that AI can become a key element in the protection of Ukrainian infrastructure. Specific regulation, standards of evidence, cross-sectoral coordination, and staff training are needed. The study’s recommendations can help authorities and infrastructure operators increase resilience and ensure the lawful and effective implementation of AI.