What is Synthetic Data?

Synthetic data is data that has been artificially generated — by algorithms, statistical models, or generative AI — to replicate the structure, patterns, and statistical properties of real-world data, without containing actual records about real people. It allows organizations to use realistic data for development, testing, and machine learning without exposing private information.

The Problem Synthetic Data Solves

Training machine learning models and building software requires large amounts of realistic data. But real data about real people carries serious risks:

• Privacy violations — Real datasets contain personally identifiable information (PII) that, if leaked or mishandled, exposes individuals to harm
• Regulatory constraints — GDPR, HIPAA, and similar laws restrict how personal data can be used for development and testing
• Re-identification risk — Even "anonymized" datasets can be re-identified by combining them with other available information
• Data sharing barriers — Organizations cannot freely share real customer data with vendors, researchers, or partners

Synthetic data sidesteps these problems. Because it is generated rather than observed, there are no real individuals behind the records — and therefore no privacy exposure in the traditional sense.

How Synthetic Data Is Generated

Statistical methods: The simplest approach. Analyze the statistical distribution of the real data (means, variances, correlations between variables) and generate new records that match those distributions. Effective for tabular data with clear structure.

Generative Adversarial Networks (GANs): A neural network architecture where two networks compete — one generates data, one tries to distinguish generated from real. Over thousands of iterations, the generator produces increasingly realistic data. Widely used for images and complex structured data.

Variational Autoencoders (VAEs): Encode real data into a compressed representation, then decode new samples from that space. Produces diverse synthetic records that follow the underlying data structure.

Large Language Models (LLMs): Can generate realistic synthetic text, conversations, documents, and structured data from prompts. Used increasingly for NLP training data and synthetic customer interaction logs.

Use Cases

Healthcare and medical research: Medical records are among the most sensitive personal data in existence. Synthetic patient data allows researchers to train diagnostic models, develop clinical software, and share datasets across institutions without exposing real patient records. Several major health systems and medical AI companies now use synthetic EHR (Electronic Health Record) data as standard practice.

Financial services: Synthetic transaction data enables fraud detection model training, stress testing, and software development without using real customer financial data. Banks subject to strict data sharing restrictions can collaborate on model development using synthetic data.

Software testing: Development and QA teams need realistic data to test systems. Synthetic data provides production-realistic test datasets that can be freely shared across teams, contractors, and cloud environments without privacy concerns.

Autonomous vehicles and robotics: Generating synthetic sensor data, camera footage, and edge-case scenarios — particularly rare or dangerous situations — allows training models on experiences that would be difficult or dangerous to collect in the real world.

Privacy Caveats

Synthetic data is not automatically private. Several risks remain:

Membership inference: It may be possible to determine whether a specific individual's data was in the training set used to generate the synthetic data, even if they are not directly represented in the output.

Mode collapse and outlier exposure: If the real data contains rare individuals with unusual characteristics, synthetic data generators may inadvertently reproduce those characteristics closely enough to enable re-identification.

Overfitting: A generator that has "memorized" the training data may produce records nearly identical to real ones, defeating the privacy purpose.

The privacy guarantee of synthetic data depends heavily on how it is generated. Synthetic data produced with differential privacy guarantees provides mathematically rigorous privacy protections; synthetic data produced without those guarantees may offer weaker protection than assumed.

Synthetic Data and AI Regulation

As regulations like the EU AI Act and GDPR increasingly govern how personal data can be used to train AI systems, synthetic data is emerging as a compliance tool. Organizations can demonstrate that their AI systems were trained on data that does not contain personal information — avoiding the legal obligations that come with processing personal data for model training.

This is driving investment in synthetic data platforms: companies like Gretel, Mostly AI, and Tonic have built commercial synthetic data generation products specifically for compliance-conscious organizations.

The Bottom Line

Synthetic data is a genuine privacy-enhancing technology when used properly. It enables valuable use cases — medical research, fraud detection, AI development — that would otherwise require collecting and processing large amounts of personal data. The key qualifier is "when used properly": the privacy guarantee depends on the generation method and must be verified, not assumed.