Generative AI is rapidly producing text, images, and videos, leading to a critical question: how can users differentiate real content from fake? The concept of Truth UI aims to address this by making content provenance clear and trustworthy. Victor Churchill, an expert in the field, emphasizes that while standards like C2PA (Content Credentials) provide metadata about authorship and AI involvement, users often remain skeptical of verification labels. Trust hinges on understanding the context behind these labels, not just their presence. Truth UI employs a method of progressive disclosure to avoid overwhelming users. Initially, a simple visual indicator presents itself. If users engage further, they can access source or author information, and if necessary, they can dive into the complete provenance data. This gradual approach helps build trust through clarity rather than pressure. Key principles include relevant information display, neutral visual language, and user control over data exploration. Balancing transparency and usability is essential to avoid information fatigue or suspicion. For a robust Truth UI, authenticity must be integrated into the product’s design system rather than treated as an add-on. This involves using consistent visual indicators, clear interaction rules, and accessible language. The underlying technical framework, supported by cryptographic signatures, should remain hidden from users, focusing instead on delivering a clear, informative experience. When design and engineering collaborate effectively, C2PA can enhance user experience instead of merely serving as compliance. Looking ahead, the goal is for authenticity to remain intact as content moves across platforms. Achieving this requires interoperable provenance systems with shared visual conventions and user control over displayed information. As Truth UI evolves, it is becoming a standard expectation, similar to security indicators we see today on websites.