Synthetic Data in AI : How it Works and More

What is Synthetic Data in AI?

A basic definition of synthetic data in the context of AI is artificially generated data that mimics real-world data but is created using algorithms rather than collected from actual events or observations. It’s used to train or test AI models when real world data is scarce, sensitive, or expensive to obtain.

Common techniques for creating synthetic data include running simulations, using generative models like GANs or diffusion models, or deploying rule-based systems that create the data. Synthetically generated data is valuable for enhancing privacy, balancing datasets, or creating edge cases that are rare in real data.

Synthetic Data Generation Explained

Synthetic data is artificially generated, not collected from real-world events. It is designed to replicate the statistical properties and patterns of real data points without exposing actual sensitive or proprietary information.

How is synthetic data generated?

Synthetic data is generated using several methods, depending on the type of data (images, text, tabular, etc.):

Rule-based generation. With this technique, predefined rules, logic, or templates are used to generate synthetic data. For example, these kinds of parameters can generate fake names, addresses, and phone numbers using formatting rules to create basic test data, in form validation, and for mock APIs.

Simulation-based generation. Mathematical models and physics-based simulations are also well-suited for generating synthetic data to simulate traffic flow, weather conditions, time series data, or financial market behavior. This type of synthetic data is ideal for use by engineers working in robotics and the design of autonomous vehicles, and for data scientists modeling risk.

Machine learning–based generation. There are several versions of this type of synthetic data generation with generative AI:

• Generative adversarial networks (GANs) house two competing neural networks: a generator that creates data and a discriminator that evaluates its realism. They are common in image, video, and audio data generation, for example creating realistic synthetic human faces or medical scans.
• Variational autoencoders (VAEs) encode data into a compressed latent space and then decode it back. They are used for more stable, interpretable generation, for example in text or with tabular data.
• Diffusion models generate data by gradually denoising a random signal to form structured outputs. These are the leading method for generating ultra-realistic images, and are used by AI systems such as DALL·E and Stable Diffusion.
• Large language models (LLMs) can be used to generate synthetic text data, conversation logs, or even code. For example, ChatGPT can generate customer support conversations for chatbot training.

The Different Types of Synthetic Data

Synthetic data using generative AI can take various forms depending on the type of real-world data it mimics and the use case it serves. Here’s a breakdown of the main types:

Tabular synthetic data. This is structured synthetic data similar to what you would find in spreadsheets or databases—for example, synthetic customer records with fields like name, age, income, and transaction history. Tabular gen AI synthetic data is often used in financial services, healthcare, and analytics tools designed to preserve privacy. Tools like synthetic data vault (SDV) are designed to generate this kind of data.

Image synthetic data. This data includes artificial images of vehicles, faces, tumors, or manufacturing defects generated or augmented for training computer vision models. It can be AI generated using GANs, diffusion models, or 3D rendering/simulation, and used for technical imaging, facial recognition, or training autonomous vehicles, to name a few common examples.

Video synthetic data. These simulated or generated video sequences are typically used to train models for autonomous driving scenarios, processing crowd movement for surveillance and behavior tracking, or gesture recognition in robotics simulation.

Audio synthetic data. These data include artificially generated speech, sounds, or acoustic environments. For example, synthetic speech with a range of accents or emotional tones might be used to train voice assistants and speech recognition tools, or for audio watermarking and noise modeling.

Text synthetic data. Artificially generated natural language such as fake news articles, customer service conversations, or code snippets can be used to train natural language processing (NLP) models, chatbots and virtual assistants, and applications for language translation and summarization.

Time-series synthetic data. Synthetic data and generative AI can be used to create data with temporal order. This is often used to train weather or stock forecasting or anomaly detection tools in cybersecurity simulations.

Geospatial synthetic data. Synthetic location data, maps, or GPS traces can simulate pedestrian paths, delivery routes, or urban traffic for use in planning smart cities or location-based services testing.

Synthetic Data vs Data Masking

Synthetic data is artificial data created to replicate the statistical patterns of real data, while masked data is real data that has simply been altered to make it anonymous to protect sensitive information. While generative AI with synthetic data uses models, rules, or simulations, masking is based on real data, with sensitive parts merely scrambled, hidden, or replaced.

For example, in a medical setting, a hospital might turn to synthetic data generation using generative AI to create synthetic MRI scans of brain tumors for AI model training without exposing real patient data. Data masking techniques might simply go through existing records and remove any identifiable details to keep the data’s utility while protecting privacy and ensuring compliance. Obviously, the privacy risk in the former situation is lower, because there is no direct link to real data.

Synthetic Data vs Data Augmentation

Similar to the above comparison, synthetic data is artificially created data that is entirely new, while data augmentation results in modified versions of existing real data to expand the dataset. Synthetic data is created from scratch using techniques like rules and simulations, while data augmentation transforms real data, for example by cropping or adding noise for a specific application.

It is possible to augment synthetic data as well as real data. Standard and synthetic data augmentation is useful for things like flipping images for vision tasks or paraphrasing text for NLP tasks.

What are the Benefits of Synthetic Data Generation?

There are various benefits of using generative AI synthetic data:

Privacy. Avoids risks associated with using personal or sensitive data contrary to regulations such as HIPAA or GDPR.

Synthetic data augmentation. Augmenting synthetic training data expands datasets, especially for rare or edge cases.

Cost and speed. It is easier and faster to generate data than it is to collect real-world data, and makes it possible to generate data at scale, instantly.

Balances and enriches datasets. Generative AI and synthetic data help correct class imbalance in datasets, for example discerning fraudulent and non-fraudulent transactions or introducing more fraud examples, or offering synthetic data points from underrepresented minority groups. Controlled variation introduced by synthetic data can reduce bias and improve generalization.

Scarcity. Using generative AI for synthetic data is ideal in domains where data is hard to obtain, such as rare diseases research or for edge cases in autonomous driving. It also enables testing under scenarios that are rare, dangerous, or unethical to reproduce in real life.

Faster iteration and prototyping. Synthetic data researchers can build and test models quickly without waiting for real-world data pipelines.

Although there are many advantages to using generative AI to create synthetic data, there are some challenges associated with this practice as well:

Realism, bias amplification, and validation. Synthetic data must closely match real data in structure and behavior. Yet if trained on biased real data, synthetic data can reproduce or even amplify it. If synthetic data doesn’t accurately reflect real-world distributions, models trained on it may underperform, fail to generalize, or overfit to synthetic artifacts. And it’s difficult to prove that synthetic data performs the same as real data without large-scale testing.

Technical complexity. High-quality data generation requires advanced techniques, domain expertise, and tuning. Many domains, especially those that demand their own high-level expertise such as science, law, or finance, are especially hard to synthesize accurately.

Regulatory and legal ambiguity. While synthetic data helps with privacy, its legal status in certain regulatory environments is still evolving. Many industries require explainability and traceability that synthetic data may not easily offer.

Synthetic Data Use Cases

Here are a few examples of why synthetic data has become increasingly valuable across industries, especially where data is sensitive, scarce, or expensive to collect:

Healthcare and life sciences. AI synthetic data can generate synthetic X-rays, MRIs, or CT scans to train machine learning models while protecting patient privacy. Synthetic data can also simulate patient records, such as artificial data for testing health IT systems without violating HIPAA. AI and synthetic data can also augment small datasets for conditions with few real cases, such as in the rare disease space, or where it is dangerous to experiment with real cases.

Financial services. Synthetic data for AI training can simulate fraudulent transactions to train detection systems more effectively. Synthetic data and AI can create synthetic credit histories or loan applications to test risk scoring models. And synthetic data anonymization can safely and privately share mocked-up substitutes for sensitive customer data with third parties or vendors that contain no personally identifiable information (PII).

Autonomous vehicles and robotics. Synthetic data used to train AI can generate edge-case scenarios that involve jaywalking or accidents in bad weather to train perception and planning systems. And synthetic virtual environments can train warehouse or home robots to navigate more effectively.

Artificial intelligence and machine learning development. Synthetic data in AI is used for model testing and benchmarking. Synthetic data for machine learning can be used to enable model training where real data cannot be accessed due to privacy-related regulatory or ethical constraints. And generating synthetic AI data with examples of underrepresented classes such as minority dialects or rare behaviors can improve results and training outcomes.

Retail and e-commerce. Synthetic data can be used to create artificial shopping sessions that allow users to analyze buyer patterns. Synthetic datasets can help test recommendation algorithms before new product rollouts. And synthetic data can be used in reviews and chats to generate realistic user queries and feedback for training customer service chatbots.

Cybersecurity. Companies can use synthetic data to train AI models for intrusion detection systems with created network logs or malware traces. And synthetic phishing emails or malicious activity patterns can be used in red team testing and threat modeling.

Gaming and virtual reality. Synthetic data can simulate game environments or player behavior to train adaptive AI agents, or auto-generate characters, terrains, or narratives for game development.

How WEKA Works With Synthetic Data for Enterprise AI

Synthetic data workloads drive very different I/O patterns compared to traditional model training. NeuralMesh™ by WEKA is designed to handle these complex patterns with ease. Unlike static datasets that are read sequentially during training, synthetic data generation often involves dynamic, bursty, and bidirectional I/O: generating data in real-time, writing it to disk, and immediately reading it back into GPU pipelines for validation, augmentation, or training. These unpredictable and concurrent access patterns can overwhelm legacy storage systems—but not NeuralMesh.

NeuralMesh delivers high-throughput, low-latency I/O across mixed workloads, making it ideal for synthetic data pipelines that combine simulation, rendering, and model training in a single environment. Its distributed architecture ensures consistent performance at scale, even as I/O patterns shift between reads, writes, and metadata-heavy operations. This flexibility allows AI teams to generate, store, and consume synthetic data without hitting performance bottlenecks—enabling faster iteration, more diverse datasets, and ultimately, better model outcomes.