ai-data-retention

name	ai-data-retention
description	Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.
license	Apache-2.0
metadata	{"author":"mukul975","version":"1.0","domain":"privacy","subdomain":"ai-privacy-governance","tags":"ai-retention, machine-unlearning, model-versioning, training-data-deletion, retraining, storage-limitation"}

AI Model Retention and Unlearning

Overview

GDPR Art. 5(1)(e) storage limitation requires that personal data be kept no longer than necessary for the processing purpose. For AI systems, this creates complex retention challenges: training data used to build a model may no longer be needed once training is complete, but the model itself encodes information about the training data. Machine unlearning — the process of removing the influence of specific data from a trained model — is an emerging field that addresses the gap between deleting training data and eliminating its influence from model parameters. This skill provides retention policies, deletion verification methods, and machine unlearning techniques for AI compliance.

AI Data Retention Categories

Data Category	Description	Retention Consideration
Raw training data