Data retention defines how long user and system data is stored before it is deleted. In AI systems, this includes conversation logs, model inputs and outputs, and any personal data processed during interactions.
Retention policies must balance operational needs against privacy regulations like GDPR and sector-specific requirements. Keeping data longer than necessary increases exposure in the event of a breach. Deleting it too quickly eliminates the audit trail needed for compliance, debugging, and model improvement. The right policy depends on the data type, regulatory context, and operational requirements.
