Synthetic AI is used to generate synthetic data that imitate real-world data. It is created using statistical or ML techniques and aims to learn the statistical properties and structure of real-world data. When real-world data is scarce, expensive, or difficult to obtain, it can easily be substituted with synthetic data. It can also augment existing data or generate data for training and testing AI/ ML models without compromising the privacy or security of the original data. By mimicking real-world scenarios, researchers or data analysts can avoid violating data protection regulations and minimize the risk of data leaks or privacy breaches. 

Different methods to produce synthetic data include generative algorithms, statistical models, and other techniques that imitate the statistical properties and patterns found in the source data.

Synthetic data is increasingly being used in various fields such as healthcare, finance, and cybersecurity, which are highly regulated industries, sensitive data is involved, and privacy is a major concern. However, there may be limitations to its usefulness and applicability. Hence, synthetic data is not a perfect replacement for real-world data. It is essential to assess the reliability and precision of synthetic data before implementing it in any scenario.