Full-Process Protection Technology Path for Copyright Risks in Generative AI Training Data

The rapid iterative development of generative AI technology is highly dependent on the supply of massive training data. The industry generally uses crawlers to grab Internet copyright works on a large scale for pre-training, which has led to global copyright disputes. The risk of copyright infringement in the acquisition and use of training data has become the core challenge restricting the development of industrial compliance. This article focuses on the complete life cycle of training data and explores three cutting-edge technological paths: pre-filtering of training data, in-process training constraints, and post-detection and evidence collection. Analyze the underlying operating principles of various technologies, conducts horizontal quantitative comparisons of mainstream technologies from four dimensions: detection accuracy, computing power cost, black-box adaptability, and judicial evidence collection effectiveness, and clarifies the practical bottlenecks of the existing protection system. The multi-layer technology defense and intellectual property system collaborative integrated protection framework is constructed in this article. It can achieve full process risk management of training data, reduce the probability of copyright infringement of large models, improve the efficiency of infringement tracing and evidence collection, and balance the protection of creator rights and the needs of AI technology innovation and development. This study can provide a complete theoretical basis for the construction of data compliance pipelines for large model enterprises, the development of industry compliance technical standards, and the improvement of digital supervision of intellectual property rights.