Application of Generative Artificial Intelligence (GenAI) in Law Enforcement Research

Abstract

Introduction: Generative Artificial Intelligence (GenAI) represents one of the most significant technological innovations of the 21st century. While its application in law enforcement is still in its early stages, it has the potential to become a long-term strategic tool in law enforcement research.

Objectives: This study aims to facilitate the domestic adoption of GenAI technologies in the law enforcement sector. It explores the key technological components necessary for the effective implementation of GenAI-based research, including training data, context window size, prompt strategies, hallucination risks, and validation processes. Additionally, it addresses the responsible application of GenAI, considerations of scientific reliability, and the transformation of researchers’ roles. The technological potential is illustrated through recent international law enforcement research examples.

Methodology: The study is based on a targeted review of over 40 scientific and policy documents from the period 2016–2025, employing a multilingual search strategy.

Results: The research identified the core technological components that determine the usability and reliability of generative artificial intelligence. The application of GenAI in law enforcement has already emerged in practice, but international examples are typically linked to pilot projects and are regarded more as technological curiosities than routine tools. International case studies showcase innovative GenAI applications, such as identifying psychological patterns - e.g., manipulative strategies, threatening or coercive communication forms - uncovering argumentation structures, and rapidly processing digital investigative documents.

Conclusion: The true potential of GenAI can unfold when users possess adequate knowledge of its functioning and are capable of applying it consciously within regulated frameworks. The successful integration of GenAI into scientific work requires a methodological paradigm shift and an expansion of researchers' roles in directions where the researcher simultaneously functions as a technological tool user, critical validator, and ethical decision-maker. This approach ensures that results generated by GenAI remain scientifically grounded, ethically acceptable, and methodologically verifiable.