As of March 2025, 40% of global companies report using artificial intelligence (AI) in their business. While the benefits offered by this transformational tool can feel nearly limitless, the reality is that AI isn’t inherently secure, especially for companies dealing with sensitive information.
AI quickly analyzes vast amounts of data to figure out patterns and provide users with a response in the shortest amount of time possible. Any data shared with the tool will be used to train the model going forward, making it a dangerous place for sensitive information. For industries that handle extremely personal data, like healthcare or law, using AI could risk client privacy.
AI is designed to quickly analyze large datasets, detect patterns, and respond in real time. But many tools train on whatever data you provide. That means sharing private information—intentionally or not—can create long-term risks, especially in regulated industries like healthcare, finance, or law.
Global Director of Solution Engineering at Apryse.
The benefits of leveraging synthetic data
AI works best with strong, structured, and relevant data. Whenever possible, real-world data is ideal—but that’s not always an option. Regulations like HIPAA and GDPR prevent teams from sharing personal data externally, including with AI models. That’s where synthetic data shines.
You’ll often see synthetic data used as a placeholder—especially when legal approvals or NDAs are still in progress. Instead of stalling development, teams can keep moving forward with stand-in data, then switch to production data later to validate the results. This keeps projects moving while staying compliant.
In other cases, synthetic data fills in the gaps. You might have real data, but not enough of it—or not enough variation to properly train your model. A good rule of thumb: you’ll need 10x more data samples than model parameters. When real data falls short, synthetic data can help augment and diversify your training set.
Considerations for using synthetic data
One common misconception is that synthetic data is just “fake” data. But in reality, it’s often based on real-world information that’s been restructured, anonymized, or generated to mirror actual scenarios. Think of it like a flight simulator—useful for training and preparation, but it’s not the same as flying a real plane. Synthetic data can help teams test and train AI models, but it shouldn’t be seen as a complete replacement for production data.
That said, it does come with risks—particularly around re-identification. If synthetic data can be traced back to the original source, the whole premise of privacy falls apart. One of the most critical steps is to ensure the original dataset is no longer stored or accessible once the synthetic version is created. Simply having the two datasets in proximity to each other creates unnecessary risk.
Another challenge is outliers. These are extreme or unusual values that can not only skew model training but also serve as clues about the original data. For example, if you’re generating synthetic banking data and one of the transactions is for $10 million while the rest are in the hundreds, that single value becomes a beacon. It’s both a modeling issue and a potential privacy concern.
In many cases, partially synthetic data can offer the best of both worlds. You might use real documents or datasets while anonymizing any personally identifiable information. For example, you could keep the visual data from an X-ray but strip out details like the patient’s name, the facility, or the diagnosis.
That way, you retain data complexity without exposing sensitive information. Finally, before using any synthetic dataset in a project, it’s worth having someone outside the core team take a final look. A fresh perspective can help spot anything you’ve missed—whether it’s residual identifiers, overlooked outliers, or subtle signs that the data could still be traced back to a real person.
Conclusion
Using synthetic data doesn’t have to be all or nothing. Many projects benefit from a hybrid approach—especially in early phases. In a world racing to adopt AI, it’s easy to move fast and overlook the risks. But safe, responsible model training is everyone’s responsibility.
Synthetic data isn’t just a workaround—it’s a bridge to building secure, innovative systems that respect privacy and compliance from day one.
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