Synthetic data is valuable in AI development but has limitations. While it aids in overcoming regulatory hurdles and testing models, it can either lack expressiveness or mimic real data too closely, risking security. Experts suggest using it for pressure testing before real data is utilized, but emphasize rigorous security measures must remain in place. Overall, synthetic data is beneficial when applied correctly, but not a complete solution to AI's data privacy challenges.
