Data rectification is the right to have inaccurate personal data corrected. Organizations should provide a process to verify a request, update the relevant records, and, where required, notify recipients who received the incorrect data. Accurate records matter for security as well as privacy: errors can lead to mistaken identity checks, flawed access decisions, or misleading risk assessments.
In machine-learning systems, correcting a source document may not automatically correct a model trained on it. Its influence can remain in model parameters, training artifacts, or outputs. Defenses include tracking data provenance and model versions, assessing whether retraining or targeted unlearning is needed, and testing outputs after changes. Teams should document how requests are handled and avoid assuming that changing a database record also changes a trained model.