Training Data Governance

Why It Matters

The training data defines the model's behavior, knowledge, biases, and vulnerabilities. Poor data governance leads to poisoned models, privacy violations, and compliance failures.

Governance Framework

Data Inventory

Catalog all data sources used for training and fine-tuning
Document data origin, collection method, and consent basis
Track data lineage from source through preprocessing to model

Data Quality

Deduplication to prevent memorization of repeated content
Quality filtering to remove toxic, biased, or low-quality content
Representativeness assessment — does the data reflect intended use cases?

Data Security

Encryption at rest and in transit for all training data
Access control — who can view, modify, and delete training data?
Audit logging for all training data access and modifications
Secure deletion procedures when data must be removed

Compliance

PII scanning before data enters the training pipeline
Consent verification — was data collected with appropriate consent for AI training?
Geographic restrictions — some data may not cross certain borders
Retention policies — how long is training data kept?

Data Provenance Checklist

□ Data source documented and verified
□ Collection method and consent basis recorded
□ PII scan completed — results documented
□ Deduplication applied
□ Quality filter applied — filtering criteria documented
□ Bias assessment completed
□ Data stored in access-controlled, encrypted storage
□ Data lineage traceable from source to model
□ Retention period defined and enforced
□ Deletion procedure tested and documented

AI Security Book