Artificially generated data that mimics the patterns of real data, used for testing, training models, or sharing without exposing real records.
Synthetic data can be produced by simulations, statistical methods, or generative AI models trained on real datasets. The goal is to keep useful properties, such as the distribution of ages or purchase amounts, without copying actual individuals' records.
A hospital software team might use synthetic patient records to test a new scheduling feature, so developers never handle real health information. AI teams also use synthetic data to cover rare situations, such as unusual fraud patterns or uncommon road conditions for driver-assistance systems. Synthetic data has limits. If it is generated poorly, it can miss important real-world patterns or carry over biases from the source data, and some methods can leak details of the original records if not designed carefully.
The policies, roles, and processes that determine how an organization's data is managed, protected, accessed, and kept accurate.
Machine LearningA branch of AI in which computers learn patterns from data to make predictions or decisions without being explicitly programmed for each case.
Personally Identifiable InformationAny information that can identify a specific person, alone or combined with other data, such as a name, email, or ID number.
What synthetic data is, how it is made, where it helps (testing, privacy, AI training) and the risks of unrealistic or leaky data, plus a checklist for using it safely.
When HIPAA applies to your app, what the Privacy, Security and Breach Notification Rules require, how BAAs work, and how to build the technical safeguards into your software.