AI validation is the testing and verification done before an AI system is allowed to support clinical decisions. It checks whether the model performs accurately on relevant data, behaves consistently, and fails safely under expected conditions. In cybersecurity terms, validation is part of trustworthy deployment: a model that is not verified can mislead clinicians, expose patients to harm, or create an easy path for abuse through bad data, weak controls, or unreviewed updates.
In real environments, validation includes testing the training and test data, checking model versioning, reviewing outputs against expert decisions, and confirming audit logs and access controls. Defenders also look for threats such as data poisoning, configuration drift, and tampering with model pipelines. In clinical decision support, AI validation helps ensure the tool is supervised by humans, traceable, and safe to use alongside other protected health systems.