Synthetic data in AI refers to artificially generated data that mimics real-world data, created through algorithms or computer simulations. This type of data is used primarily to train machine learning models where real data is either unavailable, insufficient, or sensitive. Synthetic data can be generated using various techniques including generative adversarial networks (GANs), variational autoencoders (VAEs), and other deep learning architectures, ensuring that the data produced is both diverse and representative of actual scenarios124.

The primary characteristic of synthetic data is that it is not derived from real-world events but is instead digitally constructed to replicate the statistical properties of genuine data. This allows for the extensive training and testing of AI models in a controlled yet realistic environment, without the risks associated with using sensitive or proprietary data. Moreover, synthetic data comes pre-labeled, which simplifies the process of model training by providing clear, accurate targets for learning algorithms23.

Synthetic data generation techniques have evolved significantly, leveraging advanced AI models and specialized algorithms to create realistic and diverse datasets. Key approaches include:

• Generative AI Models: Techniques like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Generative Pre-trained Transformers (GPT) learn complex patterns from real data to generate high-quality synthetic samples12. GANs use a generator-discriminator architecture to produce increasingly realistic data, while VAEs employ encoder-decoder networks to capture and recreate data distributions34. GPT models, trained on extensive tabular data, can generate lifelike synthetic tabular datasets12.

• Rules-Based Engines: These systems create synthetic data based on predefined business rules and relationships, ensuring generated data adheres to specific constraints and logic5. Entity cloning extracts, masks, and replicates business entity data to maintain structural integrity while preserving privacy6. Data masking techniques anonymize sensitive information, allowing for the generation of compliant synthetic data that retains statistical properties of the original dataset without exposing personal identifiers75.

Synthetic data has found diverse applications across industries, revolutionizing various business functions:

• Financial Services: Banks use synthetic data to enhance fraud detection models and simulate market conditions for risk assessment12. It enables secure data sharing with third parties and internal teams while maintaining compliance1.

• Healthcare: Synthetic patient data facilitates clinical trial simulations, healthcare analytics, and collaborative research without compromising patient privacy13. It allows for testing new treatments and optimizing care protocols2.

• Autonomous Vehicles: Companies like Waymo leverage synthetic data to simulate diverse driving scenarios, accelerating the development of self-driving technology3. This approach enables testing in thousands of virtual environments, complementing real-world trials1.

• Marketing and Product Development: Synthetic customer profiles enable personalized campaign testing and A/B testing without using actual customer data4. In product development, it aids in simulating user behavior and optimizing designs5.