We present DECORAIT; a decentralized registry through which content creators may assert their right to opt in or out of AI training and receive rewards for their contributions. Generative AI (GenAI) enables images to be synthesized using AI models trained on vast amountsofdatascrapedfrompublicsources.Modelandcontentcreators whomaywishtosharetheirworkopenlywithoutsanctioning its use for training are thus presented with a data governance challenge. Further, establishing the provenance of GenAI training data is important to creatives to ensure fair recognition and reward for their such use. WereportaprototypeofDECORAIT,whichexplores hierarchical clustering and a combination of on/off-chain storage to create a scalable decentralized registry to trace the provenance of GenAI training data to determine training consent and reward creatives who contribute that data. DECORAIT combines distributed ledger technology (DLT) with visual fingerprinting, leveraging the emerging C2PA (Coalition for Content Provenance and Authenticity) standard to create a secure, open registry through which creatives may express consent and data ownership for GenAI.