Legal Frameworks for AI-Driven Cybercrime Prevention
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https://doi.org/10.59022/ujldp.253Keywords:
AI-Driven Evidence, Cybercrime Prevention, Privacy Rights, Legal Frameworks, Black Box Problem, Risk Assessments, Cross-Border Enforcement, Data SecurityAbstract
This research aims to explore the legal frameworks necessary for integrating AI-driven evidence in cybercrime prevention while ensuring the protection of privacy rights. The study examines AI's role in evidence collection, particularly focusing on the challenges of AI surveillance capabilities and the privacy concerns surrounding data gathering. Using qualitative, doctrinal, and document analysis methods, the research analyzes how different jurisdictions address the admissibility of AI evidence in criminal proceedings. The findings highlight the challenges posed by AI's opacity, the black box problem, and the reliability of AI-generated evidence. The recommendations include conducting risk assessments, limiting data collection, ensuring informed consent, and enhancing security practices. The research suggests developing international protocols for cross-border enforcement systems to address the evolving nature of cybercrime. In conclusion, this study provides insights into adapting legal standards for AI-driven cybercrime prevention while safeguarding individual privacy rights.
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