Data has long been considered a form of digital currency. However, organizations are finding they lack adequate “cash flow” when it comes to training AI models.
Organizations collect vast amounts of data, but it’s often scattered across various legacy systems that do not communicate. It’s typically unlabeled, unstructured or unreliable, requiring significant time to clean and prepare for use. Preparing data accounts for as much as 80 percent of AI projects.
By restricting the use of sensitive information, stringent regulations such as GDPR and CCPA have caused organizations to limit data collection. According to a 2024 study conducted by MIT Sloan, data storage in the EU dropped by 26 percent as a direct result of GDPR.
Synthetic data can help organizations generate the information they need to support their AI initiatives. In fact, Gartner has predicted that most data used to train AI models will be synthetic by 2030. However, synthetic data is not a foolproof way to ensure privacy and regulatory compliance. Organizations must follow best practices to prevent synthetic data from being connected to real individuals.
How Synthetic Data Is Generated
As we explained in a previous post, synthetic data is artificially generated information that mirrors the statistical patterns, correlations and characteristics of real-world data. In theory, it doesn’t contain any real-world information. It acts as a “synthetic twin” that allows organizations to train AI without the privacy, cost or accessibility hurdles of real data.
Synthetic data is generated by training machine learning models or using statistical models or simulations to create new data points that mimic the original. Choosing the right technique depends on the type of data to be generated.
LLMs are used to generate synthetic text, while generative adversarial networks create high-fidelity data for images, audio and video. Statistical methods involve modeling the probability distributions and correlations of real datasets to generate new records that follow known patterns, making it ideal for structured data.
Agent-based modeling (ABM) generates data by simulating a complex system, with autonomous “agents” that follow defined rules and interact with one another. Because it focuses on the “why” and “how” behind individual actions, ABM produces highly realistic data for complex systems.
Why Organizations Use Synthetic Data
Generating artificial data is exponentially cheaper and faster than manually collecting and labeling real data, images or text. Synthetic data can also simulate rare edge cases or future scenarios that are difficult or impossible to capture in real-world data. To mitigate bias, developers can “rebalance” datasets by generating more samples for underrepresented groups, leading to fairer AI models.
Privacy and compliance are primary benefits of synthetic data. By removing links to real individuals, companies can comply with strict laws like GDPR or HIPAA while still retaining the analytical value of the data. However, the more accurately synthetic data mimics real data, the higher the risk that it could be reverse engineered to reveal private information.
Some synthetic data isn’t entirely synthetic. Sensitive information may be altered slightly or replaced with anonymous identifiers, but otherwise the data is statistically similar to the original. That tends to make it more useful for training AI models, but it also increases the odds that can be traced back to real people.
Ensuring Synthetic Data Is Untraceable
By following best practices, organizations can reduce the risk. Source data should be sanitized by removing or masking personally identifiable information before it is used to train the synthetic model. Intentionally altering data points, such as shifting dates or slightly changing numerical values, can prevent exact matches. Organizations should also ensure the input data is representative and diverse to prevent the model from focusing on unique, easily traceable outliers.
The AI model should learn general statistical patterns rather than memorizing individual records, else it might reproduce real data. After the synthetic data is generated, it should be tested to ensure it cannot be linked back to the original dataset.
Ideally, organizations should train a separate model to distinguish between real and synthetic data. If the model cannot differentiate them, the synthetic data is high quality and likely safe. Once the synthetic data is generated and validated, the source data should be securely deleted to eliminate the risk of leaks.
How Technologent Can Help
Technologent has a practice dedicated to data services that span management, governance, analytics and AI. We can help you create synthetic data for AI training without creating privacy and compliance risks.
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