Key Takeaway
AI data handling policies must address the unique challenge that deleting source data does not erase what a model has learned from it, requiring additional controls around model lifecycle management.
When to Use This Template
Use this template to supplement your existing data protection policy with AI-specific data handling requirements. AI workloads introduce unique data handling challenges: training data provenance, model memorization of PII, embedding storage containing derived personal data, and third-party API data processing agreements. This policy addresses these challenges directly.
Policy Sections
Extend your existing data classification scheme with AI-specific categories: Training Data (data used to train or fine-tune models, requires provenance tracking and bias documentation), Evaluation Data (data used to measure model performance, requires version control and representativeness verification), Inference Data (user inputs to AI systems, classified at the same level as the underlying data), and Model Artifacts (trained models, embeddings, and generated outputs, classified based on what the model was trained on).
Define consent requirements for AI-specific data uses: existing data consent may not cover AI training (verify with legal), new collection for AI must specify AI training as a purpose, and synthetic data generation from real data requires the same consent as the source data. Define processing rules: data minimization (use the minimum data necessary for the AI task), anonymization requirements (PII must be removed or masked before AI processing unless the AI task specifically requires it), and geographic restrictions (data must be processed in approved regions).
Define requirements for managing training data: Provenance tracking (document the source, collection method, and consent basis for all training data), Bias documentation (document known biases in training data and mitigation measures), Version control (training datasets must be versioned and reproducible), and Refresh procedures (how training data is updated, validated, and re-documented). Include requirements for training data audits that verify compliance with these standards.
Define retention periods for each data category: training data (aligned with model lifecycle plus audit period), evaluation data (retained as long as the model is in production), inference data (minimal retention, typically 30-90 days unless needed for quality monitoring). Address model memorization: when source data is deleted, assess whether the trained model has memorized PII and whether the model needs to be retrained. Define third-party data sharing rules: AI vendor data processing agreements must specify whether vendor can use your data for model training, data must not be shared with AI vendors without contractual protections.
Customization Guidance
Adapt retention periods and processing restrictions to your regulatory environment. GDPR, CCPA, HIPAA, and industry-specific regulations each impose different requirements on data processing for AI. Work with your legal and compliance teams to ensure the policy addresses your specific regulatory obligations. The model memorization section is particularly important for organizations subject to right-to-deletion requirements.
Do not assume that deleting training data satisfies a deletion request. If a model was trained on the data, the model itself may need to be retrained or retired. Document your organization's position on model memorization and deletion in consultation with legal counsel.
Version History
1.0.0 · 2026-03-01
- • Initial AI data handling policy template