Data
Synthetic Data
Synthetic data is AI-generated data that statistically mimics a real dataset without containing actual personal records. It is used to train and evaluate AI models when real data is scarce, sensitive, or legally restricted — particularly in healthcare and financial services.
Synthetic data generation carries its own risks: if the generator learns from biased real data, the synthetic data inherits that bias. Privacy guarantees require formal differential-privacy bounds, not just record anonymization. Regulators in healthcare and banking are still developing guidance on synthetic-data use in model validation.
Related terms
- Fine-Tuning — Fine-tuning is the process of training an existing AI model on additional task-specific data so its weights adapt to a narrower domain.
- Data Residency — Data residency is the requirement that data be stored and processed within a specific geographic jurisdiction.
- AI Risk — AI risk is the set of categorized hazards a deployment introduces — including hallucination, bias, data leakage, prompt injection, regulatory non-compliance, vendor lock-in, and unintended automation of harm.