The provided text explores the SYRAG™os framework, which prioritises a conscious data lifecycle to ensure that artificial intelligence remains operationally reliable and ethical. The author argues that true data governance must begin well before an AI generates an output by focusing on the four pillars of creation, usability, persistence, and reuse. This approach shifts the focus from mere model performance to the traceability and accountability of the information being processed. By treating data as a governed asset rather than just raw input, organisations can build systems that are verifiable and maintain integrity over time. Ultimately, the source suggests that the trustworthiness of AI depends entirely on the structural discipline applied to ...