The rapid advancement of generative artificial intelligence (GenAI) has introduced critical challenges for the investigation and prosecution of child sexual abuse material (CSAM). Using an exploratory mixed-methods approach, this study sought to understand the current state of, and challenges in, investigating and prosecuting GenAI CSAM. Data were collected through an internet-based survey of United States criminal justice professionals ( N = 92) who worked on criminal cases involving CSAM. A majority of respondents reported encountering GenAI CSAM in casework. In addition, the findings reveal three primary challenges: (1) insufficient and varied training and resources for identifying GenAI CSAM, (2) substantial variability in professional confidence when distinguishing between authentic and synthetic material, and (3) inconsistencies and gaps within existing federal and state legal frameworks. Overall, the results highlight a critical need for standardized training, improved access to validated forensic tools, and legal reforms that reflect the evolving nature of GenAI content. Without such efforts, the ability to distinguish between real and synthetic CSAM threatens to undermine investigative accuracy, judicial outcomes, and victim identification.
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Nagalla et al. (2026) studied this question.
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