Generative Artificial Intelligence (AI) in the form of Large Language Models (LLM) and latent diffusion image generators, has caused an intricate structural crisis in international copyright law. In particular, this crisis concerns with the unauthorized ingestion, reproduction and synthesis of protected works for machine learning training. The first challenge, evidentiary burden of proof, is a formidable obstacle in enforcing the rights of AI developers with respect to procedural IP considerations. In procedural IP considerations, the evidentiary burden of proof is a monumental and often insurmountable hurdle for enforcing IP rights of an AI developer. In essence, AI systems are algorithmic “black boxes,” and it will be very hard for plaintiffs to establish that a specific creative piece was actually used in the training process, particularly if the output of the AI system is not visually or textually similar to the input. In this paper, the authors perform a comprehensive doctrinal examination of the evidentiary issues in modern AI copyright litigation. The research examines the principles of digital forensics, which are necessarily new and sophisticated, and explores their application with respect to traditional legal concepts like “access” and “substantial similarity,” which are ultimately inadequate for the architecture of neural networks. The paper proposes a theory that the plaintiffs are forced to increasingly and exclusively depend upon sophisticated digital forensic techniques, such as metadata analysis, dataset tracing, hash matching and memorization extraction techniques to meet the evidentiary requirements of the courts without statutory requirements mandating transparency of the data sets. In conclusion, the research calls for the formal inclusion of standardised digital forensic procedures in IP litigation and suggests that there should be a legislative change in the direction of compulsory auditing of datasets to facilitate fair enforcement of copyright in the digital era.