Most companies believe synthetic data is safe.

They assume that if data is artificially generated, it no longer falls under privacy regulations. They assume it cannot be linked back to real individuals. And they assume it removes compliance risks.

But when you ask a simple question like “Can synthetic data still be considered personal data under GDPR?” — the answer is not so clear.

And that’s the problem.

In today’s world, many organizations rely on AI systems and large-scale data processing. Data safety assumptions are no longer enough. Regulators expect evidence.

Risks are becoming more complex. And your business depends on understanding where those risks truly exist.

This is where synthetic data GDPR compliance becomes critical.

What is synthetic data in AI?

At its core, synthetic data in AI is data made by computers, not collected from real people.

It is often made with algorithms or machine learning models to copy patterns in real-world data. This makes it especially useful for training, testing, and validating AI systems.

But in practice, synthetic data is not as simple as “fake data.”

It is designed to behave like real data. It reflects real patterns, relationships, and behaviors. And in many cases, it is derived from real datasets, even if the final output does not directly contain identifiable information.

Instead of raw personal data, synthetic data AI provides a simulated version of reality.

This is why it has become increasingly popular. It allows organizations to:

• Reduce reliance on real personal data
• Scale AI development faster
• Improve testing environments

But this also introduces a critical question.

If synthetic data is based on real data patterns, can it still carry privacy risks?

Synthetic data vs personal data under GDPR

GDPR defines personal data as any information related to an identified or identifiable individual.

At first glance, synthetic data seems to fall outside this definition. After all, it does not directly represent real people.

But the reality is more complex.

The key issue is not whether the data is real or synthetic. The key issue is whether an individual can be identified, directly or indirectly.

If synthetic data:

• Can be linked back to real individuals
• Preserves identifiable patterns
• Or allows re-identification through additional data

Then it may still be considered personal data under GDPR.

This is where many organizations make a mistake.

They treat synthetic data for AI as automatically anonymous. But GDPR does not define anonymity based on how data is created. It defines it based on whether identification is possible.

That distinction is critical.

Because if identification is possible, GDPR applies.

Synthetic data privacy risks

The biggest misconception about synthetic data is that it eliminates privacy risk.

In reality, synthetic data privacy risks are evolving, especially with the rise of advanced AI models. As technology advances, these risks become more difficult to detect and control.

These risks include:

Re-identification risk

This is one of the most critical concerns when evaluating synthetic data.

Even if data is artificially generated, it can sometimes be reverse-engineered or matched with other datasets to identify individuals.

This becomes more likely when multiple data sources are combined or analyzed together.

Pattern leakage

This risk is often overlooked because the data does not appear sensitive at first glance.

Synthetic data often preserves statistical patterns from real data. These patterns are essential for usability but can introduce hidden exposure.

In some cases, those patterns can reveal sensitive information. This is particularly risky in datasets involving health, finance, or behavioral data.

Overfitting in AI models

If synthetic data is too closely based on real datasets, models may unintentionally memorize and reproduce real personal data.

False sense of compliance

Organizations may assume they are compliant simply because they are not using “real” data, leading to gaps in risk assessment.

These risks are not theoretical.

As AI systems become more powerful, the ability to analyze, correlate, and infer information increases. This makes it easier to extract insights that may relate back to real individuals.

And under GDPR, that possibility matters.

Why synthetic data GDPR compliance is complex

Synthetic data sits in a regulatory gray area.

GDPR does not explicitly define synthetic data. Instead, it focuses on outcomes — specifically, whether individuals can be identified.

This creates uncertainty.

From a compliance perspective, organizations must assess:

• How synthetic data is generated
• What source data is used
• Whether re-identification is possible
• How the data is ultimately used

This is especially relevant when using synthetic data for AI.

AI systems often rely on large datasets and complex transformations. This makes it harder to trace:

• Where data originates
• How it is processed
• Whether it still relates to individuals

In addition, regulatory expectations are evolving.

Authorities increasingly focus on:

• Risk-based approaches
• Accountability
• Demonstrable safeguards

This means companies must be able to justify their assumptions.

Not just internally, but during audits and investigations.

Synthetic data for AI: practical compliance challenges

The use of synthetic data for AI introduces specific challenges for organizations.

In many cases, synthetic data is used for:

• Training machine learning models
• Testing systems in development
• Simulating user behavior
• Enhancing datasets

While these use cases provide clear benefits, they also create compliance risks.

For example:

A company may generate synthetic customer data for testing.

But if that data is derived from real customer datasets, the original risks may still exist.

Or an AI model trained on synthetic data may still reflect real-world behaviors in a way that exposes sensitive information.

These scenarios highlight a key issue.

Synthetic data does not remove responsibility. It shifts it.

Organizations are still responsible for:

• Understanding how data is generated
• Evaluating potential risks
• Ensuring compliance with GDPR principles

Without this, synthetic data can create hidden exposure rather than reducing it.

To address synthetic data GDPR compliance effectively, organizations need a structured approach.

This starts with recognizing that synthetic data is not automatically safe.

From there, companies should:

Assess re-identification risks

Evaluate whether synthetic data can be linked back to real individuals, directly or indirectly.

Document data generation processes

Understand and record how synthetic data is created, including source data and transformation methods.

Apply GDPR principles

This includes data minimization, purpose limitation, and security — even when working with synthetic data.

Align privacy and AI teams

Synthetic data sits at the intersection of data privacy and AI development. Both perspectives are necessary.

Avoid assumptions

Do not assume that synthetic data falls outside GDPR. Validate it.

These steps are not just about compliance.

They are about maintaining control.

From innovation to risk management

Synthetic data is often seen as a solution.

It enables faster innovation. It reduces reliance on sensitive data. And it supports the growth of AI.

But without proper oversight, it can also introduce new risks.

The challenge is not whether to use synthetic data.

The challenge is how to use it responsibly.

Organizations that treat synthetic data as part of their broader data governance strategy are better positioned to:

• Reduce compliance risks
• Improve transparency
• Build trust with customers

Over time, this approach shifts synthetic data from a perceived shortcut into a controlled and reliable tool.

Why companies are rethinking synthetic data strategies

As regulatory scrutiny increases, organizations are moving away from assumptions and toward evidence-based compliance.

Synthetic data is no longer viewed as a simple workaround.

Instead, it is treated as part of a broader data ecosystem that requires:

• Visibility
• Documentation
• Ongoing risk assessment

This shift is especially important in environments where AI plays a central role.

Because as AI capabilities grow, so does the potential for unintended data exposure.

Companies that recognize this early are better prepared.

Simplifying synthetic data compliance with Sovy

Managing synthetic data GDPR compliance requires clarity, structure, and continuous oversight.

Sovy is designed to support organizations in navigating these challenges.

Instead of relying on assumptions, teams can:

• Maintain visibility into how data is used
• Document processing activities
• Align privacy and AI workflows
• Support GDPR requirements with confidence

With Sovy Data Privacy Essentials, organizations can move past uncertainty. They can build a structured approach to data privacy, even in complex AI environments.

Synthetic data is changing how organizations approach data.

It offers flexibility, scalability, and new opportunities for innovation. But it also challenges traditional assumptions about privacy and compliance.

Under GDPR, what matters is not how data is created, but whether individuals can be identified.

This is why synthetic data GDPR compliance is becoming a critical topic.

As organizations continue to adopt AI and rely on synthetic data for AI development, the need for clarity, accountability, and control will only increase.

With the right approach, synthetic data can support both innovation and compliance.

Without it, it can create risks that are difficult to detect and even harder to manage.

What is synthetic data in AI?

Synthetic data in AI is artificially generated data designed to replicate real-world patterns without directly using real personal data.

Is synthetic data considered personal data under GDPR?

It can be, if individuals can be identified directly or indirectly through re-identification or data correlation.

What are the main synthetic data privacy risks?

The main risks include re-identification, pattern leakage, overfitting in AI models, and false assumptions about anonymity.

Is synthetic data GDPR compliant?

Synthetic data can support compliance, but it is not automatically compliant. Organizations must assess risks and ensure GDPR principles are met.

Why is synthetic data for AI risky?

Because it often relies on real data patterns, which may still reveal information about individuals or allow re-identification.

How can companies ensure synthetic data GDPR compliance?

By assessing risks, documenting data processes, applying GDPR principles, and maintaining visibility into how data is generated and used.

Does using synthetic data remove GDPR obligations?

No. GDPR obligations still apply if there is any possibility that individuals can be identified.

How can Sovy help with synthetic data compliance?

Sovy Data Privacy Essentials helps organizations manage data privacy by providing visibility, structure, and tools to support GDPR compliance in complex environments, including AI and synthetic data use cases.

If you’re looking to simplify synthetic data GDPR compliance and gain full control over your data, adopting a modern solution like Sovy is a practical and effective step forward.