Suno says it plans to add new tools intended to improve transparency around AI-generated music. According to reports, the company will embed identification marks directly into audio using audio watermarking and fingerprint-style technology. The goal is to make AI-created tracks more easily detectable, rather than relying solely on metadata or user-provided labels. The announcements are also framed as a step toward clearer provenance for music produced by AI systems, responding to ongoing concerns about how listeners and platforms can tell whether a song was generated. While coverage focuses on the technical approach—using embedded signals that can be recognized through detection methods—the sources also note that the effectiveness and sufficiency of such measures depends on how widely the detection tools are adopted and how reliably they work in real-world listening and distribution scenarios. Suno’s update centers on adding these markers so identification can be supported at the audio level.