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<p>This systematic review examines artificial intelligence-generated evidence in cybercrime investigations, analyzing peer-reviewed articles published between 2013 and 2024 to identify key trends, challenges, and opportunities for using AI-generated evidence in criminal court proceedings. Following PRISMA guidelines, 20 articles were systematically selected and evaluated for methodological rigor, research design, and contribution to confidence assessment frameworks. While AI-generated evidence offers potential benefits for forensic efficiency and pattern recognition, significant challenges remain regarding user confidence, model transparency, and legal admissibility. Critical gaps exist in standardized confidence metrics, validation frameworks, and judicial acceptance protocols. This review provides researchers and practitioners with a foundational understanding of AI-generated evidence validation, reliability assessment, and legal admissibility standards while identifying essential directions for future research.</p>

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Confidence levels of artificial intelligence generated evidence in cybercrime investigations—a survey

  • 期刊论文

  • 发表日期：

    2026年6月27日

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  • Milinda Rambel Stone,
  • Varghese Vaidyan

摘要

This systematic review examines artificial intelligence-generated evidence in cybercrime investigations, analyzing peer-reviewed articles published between 2013 and 2024 to identify key trends, challenges, and opportunities for using AI-generated evidence in criminal court proceedings. Following PRISMA guidelines, 20 articles were systematically selected and evaluated for methodological rigor, research design, and contribution to confidence assessment frameworks. While AI-generated evidence offers potential benefits for forensic efficiency and pattern recognition, significant challenges remain regarding user confidence, model transparency, and legal admissibility. Critical gaps exist in standardized confidence metrics, validation frameworks, and judicial acceptance protocols. This review provides researchers and practitioners with a foundational understanding of AI-generated evidence validation, reliability assessment, and legal admissibility standards while identifying essential directions for future research.