How is synthetic data changing model training and privacy strategies?
Synthetic data refers to artificially generated datasets that mimic the statistical properties and relationships of real-world data without directly reproducing individual records. It is produced using techniques such as probabilistic modeling, agent-based simulation, and deep generative models like variational autoencoders and generative adversarial networks. The goal is not to copy reality record by record, but to preserve patterns, distributions, and edge cases that are valuable for training and testing models.As organizations collect more sensitive data and face stricter privacy expectations, synthetic data has moved from a niche research concept to a core component of data strategy.How Synthetic Data Is Changing…