Maintaining GCP Data Integrity in an AI-Enabled Trial

Good Clinical Practice rests on data integrity: data must be Attributable, Legible, Contemporaneous, Original, and Accurate — the ALCOA+ framework. Introducing AI into data management raises a fair question: how do these principles hold when algorithms clean, code, or flag data? The answer is governance. Every AI process touching trial data must be validated, version-controlled, and fully auditable. When a model flags an outlier or suggests a query, that action must be attributable and reversible, with a clear human decision recorded. Black-box automation has no place in a regulated trial. Taragal Research designs its AI-enabled data workflows around auditability first. Models assist data managers; they do not silently alter records. This keeps trials inspection-ready and preserves the trust that sponsors and regulators place in the data.