Copyright Enforcement in the Age of Generative AI

Abstract

Generative artificial intelligence (AI) raises significant copyright enforcement issues regarding source acquisition, training data retention, optimization, and output. Current study examines IP rights enforcement via mixed doctrinal and descriptive and documentary methodology where opaque technology intersects with undefined legal standards. The research reviews 97 recorded generative AI cases from 2022 to 2026, including 13 judgments or procedural entries and 13 legislative or other government documents. A total of 132 recorded instances were counted, and duplicate and irrelevant entries were excluded. The proceedings register reflects 79 U.S. cases (81.4% of the total), of which 51 were filed in the Southern District of New York (SDNY). Recorded filings increased from two in 2022 to 37 in the incomplete observation period of 2026, while 62 proceedings show an active public-status label. The largest single uncombined enforcement category concerns training-use allegations, with 32 entries. It neither establish the prevalence of infringement nor evaluate the success of related litigation. Doctrinal analysis reveals that certain evidentiary and remedial issues arise from the reproduction right, training exceptions, rights reservations, human authorship, and copyright management information, among others. The cited judicial developments underscore the importance of distinguishing between allegations and holdings, the source of the training material and subsequent training, and interim versus final decisions. The paper presents a framework that integrates identification and traceable acquisition of rights, their disclosure and legal defence, and the proportionality of the relief sought. It supports documented acquisition practices, interoperable rights reservations, practical licensing pathways, and access to courts while maintaining confidentiality. The framework addresses enforcement barriers and structures responses in a controlled manner. Further research is needed to verify dockets and track outcomes to substantiate stronger claims.