Synthetic data and real privacy: safeguarding AI in Australia

As artificial intelligence (AI) moves beyond experimentation to industrial-scale deployment, questions around privacy, ethics and accountability continue to be asked. With so much attention focused on what AI can do, Data Privacy Day serves as a timely reminder for Australian leaders to step back and focus on what responsible AI ultimately depends on: the data itself and how it is protected.

As we know, beyond just processing information, AI systems learn, identify patterns, support decisions and inform action. To do that effectively, they rely on large volumes of high-quality data. But unlike other resources, data comes with clear obligations. It is personal, regulated and increasingly scarce. As AI adoption accelerates to fuel efficiency and competitiveness, so does the responsibility to safeguard data.

When privacy and data governance aren’t handled correctly, AI systems hold the potential to unintentionally amplify bias, expose sensitive information, or lead to breaches that undermine customer and community confidence.

Unlocking AI potential while recognising its limits

One way organisations are looking to manage these privacy risks is through synthetic data: artificially generated datasets that reflect real-world patterns without exposing personal information. As interest and investments grow, synthetic data is becoming a practical way for teams to test, train and develop AI systems without relying solely on sensitive or highly regulated datasets. Applied effectively, this privacy-conscious tool can reduce pressure on sovereign and personal data while still supporting innovation at scale.

However, synthetic data is not a silver bullet. It still depends on the quality of the real data it’s modelled on and can inherit bias or flawed assumptions if governance is weak. Poorly designed synthetic data sets may even pose re-identification risks.

This is why privacy-conscious governance remains essential — regardless of whether it is synthetic or real-world data. Clear metadata, lineage, access controls and policy enforcement can ensure that data is used responsibly and transparently across hybrid and multi-cloud environments.

Synthetic data fills the gaps, while real-world data keeps it grounded in reality

In practice we are seeing growing interest in hybrid data approaches that combine real and synthetic data. Real-world data provides the nuance, edge cases and variability needed to anchor models in reality. Synthetic data — generated quickly and at scale — can then be used to fill gaps, simulate uncommon scenarios, and enable safer collaboration across teams, agencies or partners without exposing sensitive records.

This balanced approach is also increasingly appearing in Australian policy and practice. The government’s recent AI technical standard recommends using a mix of data sources — real-world and synthetic — to support appropriate virtual and real-world testing (see Criterion 94). In health care, initiatives such as the SynGP500, a clinically grounded synthetic dataset aligned with real-world GP consultations, show how organisations can innovate while maintaining patient privacy and complying with regulatory expectations.

Turning governance into growth this Data Privacy Day

In this Data Privacy Day, the opportunity for Australian organisations is clear: invest in private AI and robust data governance strategies to unlock the economic potential of tomorrow. Synthetic data can play a valuable role in accelerating innovation safely, but sustainable, responsible AI starts with protecting the people behind the data.

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