Synthetic data refers to artificially generated information that mimics the structure and patterns of real-world data but does not link to actual individuals, offering privacy benefits over traditional anonymization techniques. It is increasingly used to train AI systems, correct biases in datasets, and expedite the development of proofs of concept, with Gartner predicting that by 2024, a significant portion of AI training data will be synthetic. Although synthetic data can closely replicate the accuracy of real data without posing re-identification risks, it also presents challenges, such as the inability to trace back to real-life individuals for practical problem-solving or exposing competitive insights. Despite these challenges, synthetic data is seen as a promising tool for enhancing data privacy and utility, particularly when combined with advanced techniques like homomorphic encryption, offering new opportunities for collaboration across industries while maintaining data security.