Webinar highlights: Accelerating domain-specific AI model training with private data

The era of publicly available datasets driving AI advancements is evolving. The next frontier in AI lies in domain-specific models trained on private, sensitive data—particularly in highly regulated industries like healthcare, finance, and government. In our recent webinar, industry experts from NVIDIA and Tonic.ai explored how organizations can leverage private datasets while maintaining strict compliance and security.

Featuring Brad Genereaux, Global Healthcare Lead at NVIDIA, and Adam Kamor, Co-Founder & Head of Engineering at Tonic.ai, the discussion provided valuable insights into the challenges and opportunities of training AI models on sensitive, high-quality datasets.

Here are our key takeaways from the conversation.

The shift from public data to domain-specific AI

AI has come a long way from being trained on vast, publicly available datasets. While these sources helped establish early breakthroughs in artificial intelligence, they often fall short when applied to highly specialized industries or organizations that require domain-specific accuracy.

The conversation emphasized that AI must evolve beyond one-size-fits-all training approaches. Instead, models should be trained using data that captures the nuances of specific industries and businesses, allowing for greater accuracy and improved decision-making for key stakeholders. In healthcare, domain specific models lead to better clinical decision support, which leads to better health outcomes.

With specialized models becoming the future of AI, enterprises that rely on real-world, domain-specific data will gain a competitive advantage in innovation and efficiency.

Overcoming challenges with sensitive data

Despite the benefits of training AI models on private datasets, working with sensitive information like personally identifiable information (PII) and protected health information (PHI) presents complex challenges. Strict compliance regulations such as HIPAA, GDPR, and CCPA limit how organizations can collect, store, and use this data, making AI development in regulated industries particularly difficult.

Beyond legal constraints, enterprises also face data accessibility issues and security risks when handling private information. Many AI teams struggle with obtaining enough high-quality data without violating privacy policies.

"You need a way to essentially train your models on data that is not going to leak or regurgitate any sensitive information from your training data set," explained Adam Kamor of Tonic.ai.

The key is finding innovative ways to extract value from sensitive data while preserving privacy and regulatory compliance.

Companies that fail to address these challenges risk falling behind, while those that integrate privacy-first AI solutions will be able to safely leverage their enterprise data to build smarter, more effective AI models.

How synthetic data enables AI innovation

One of the most promising solutions to the private data challenge is synthetic data—realistic, datasets synthesized from real data that retain the statistical properties, semantic meaning, and context of the original data while removing or replacing identifiable information. This technology is unlocking new possibilities for AI model training, particularly in industries where data privacy is non-negotiable.

According to Adam Kamor, synthetic data isn’t just a workaround—it’s a critical enabler of AI innovation in regulated industries. By using synthetically generated datasets, companies can train AI models without exposing real-world sensitive information, allowing for compliant, scalable, and high-quality data generation.

"They're using one of our solutions, Tonic Textual, to generate a safe training data set devoid of customer PHI. It's all synthesized so that they can train their model without risk of putting PHI into the actual model weights,” Kamor said.

The ability to generate high-fidelity, domain-specific synthetic data is transforming how enterprises approach AI training, model validation, and privacy protection.

Real-world applications: AI in healthcare

The impact of synthetic data and secure AI training is highly evident in healthcare, where data privacy regulations are among the strictest. The webinar highlighted how AI is revolutionizing medical decision making, diagnostics, and personalized treatment—but only if models can be trained on reliable, privacy-compliant datasets, based on real-patient medical data.

This breakthrough means AI can assist doctors in diagnosing diseases, identifying patterns in patient data, and even predicting health outcomes—all while safeguarding patient privacy.

One example discussed in the webinar was how synthetic EHR (Electronic Health Record) data is being used to train predictive AI models. By replacing real patient information with synthetic equivalents, healthcare organizations can develop accurate AI-driven solutions without compromising compliance.

With these advancements, healthcare is emerging as a leading example of how domain-specific AI can drive industry-wide innovation—and similar transformations are happening in finance, cybersecurity, and other regulated fields.

The future of AI: secure, domain-specific model training

As AI adoption continues to expand across industries, companies must navigate the delicate balance between innovation and data security. Enterprises that successfully leverage private, domain-specific data while maintaining compliance will lead the next generation of AI advancements.

The future belongs to businesses that can train specialized AI models while safeguarding sensitive data.

Moving forward, companies should focus on three key strategies:

1. Investing in data governance and synthetic data technologies to protect privacy while enabling AI development.
2. Ensuring regulatory compliance by implementing AI solutions designed for security-first data handling.
3. Training models on domain-specific datasets to achieve higher accuracy and industry-specific insights.

As Genereaux put it, “It’s absolutely important that we understand that we’re using our data correctly and appropriately.” At the end of the day, companies that get this right will set the standard for ethical and effective AI.

Watch the Full Webinar

Want to dive deeper into domain-specific AI model training and the future of synthetic data? 

Watch the full discussion between NVIDIA and Tonic.ai.