Synthetic data and AI: what you need to know

With the increasing adoption of Artificial Intelligence in different sectors, the demand for quality, diverse, well-structured, and above all, secure data is also growing. It is in this context that the concept of synthetic data emerges as a powerful alternative for training AI models without the risks and limitations associated with real data.

Instead of relying exclusively on sensitive, expensive, or scarce information, synthetic data is artificially generated but follows statistical, behavioral, and structural patterns similar to those of real data. They allow simulating complex scenarios, ensuring privacy, and accelerating the development of machine learning-based solutions.

In this article, we will explain what synthetic data is, how it connects with artificial intelligence, what its benefits and risks are — and why it is gaining traction in companies looking to scale the use of AI with more agility, security, and governance.

What are synthetic data?

Synthetic data are data artificially generated by algorithms to simulate real data. Instead of collecting information directly from users, systems, or sensors, synthetic data is created based on statistical rules, probabilistic distributions, or machine learning models, which replicate patterns present in the original data — but without carrying personal, confidential, or sensitive information.

In other words: they are fictitious but realistic data.

These data can be structured (like spreadsheets with customer information or financial transactions), unstructured (like images or videos generated by AI), or even multimodal synthetic data, combining different formats for complex testing and training scenarios.

How is synthetic data generated?

There are different approaches to generate synthetic data, and the choice of technique depends on the type of data, the objective, and the desired level of complexity. The main approaches include:

• Traditional statistical models: Based on the distribution of real data, useful for generating large volumes of tabular data (e.g., age, income, location).
• Machine learning-based models: Such as generative adversarial networks (e.g., GANs), which learn patterns from an original dataset and generate new samples with high fidelity.
• Computational simulations: Used in areas like robotics, games, or urban mobility, where simulated environments are created to generate large-scale data.

What is the difference between Synthetic Data and artificial data?

The terms synthetic data and artificial data are often used interchangeably, but there is a subtle — and relevant — difference depending on the context in which they are applied.

Synthetic data: realism with purpose

As we mentioned, synthetic data refers to data artificially generated based on real patterns, using statistical methods or AI models to reproduce the behavior and structure of the original data without using real instances.

In other words, synthetic data is false from an individual perspective but true from a statistical and functional perspective.

Artificial data: any data that is not real

The term artificial data is broader and more generic. It can refer to any data that was not directly collected from the real world — including completely random examples or manually fabricated data, without regard to statistical patterns or realism.

Example:

• A developer filling a test database with generic names (“John Doe”, “Jane Doe”), random numbers, or invented data is using artificial data — but not necessarily synthetic data.

In summary:

Although all synthetic data is a type of artificial data, not all artificial data is considered synthetic data. The difference lies in the degree of sophistication, fidelity to real patterns, and applicability for AI projects with strict technical and ethical requirements.

What are the benefits of using Synthetic Data?

The use of synthetic data has gained traction in companies looking to scale the adoption of artificial intelligence with security, agility, and fewer regulatory barriers. By simulating data with high fidelity to real patterns, but without the risks associated with sensitive information, synthetic data opens up new possibilities for innovation — especially in environments where privacy and data availability are constant challenges.

Check out the main benefits of using synthetic data:

1. Guaranteed privacy

Since synthetic data are artificially generated and do not have a direct link to real individuals, they offer an extra layer of security for projects involving personal or sensitive data, as in health, finance, and energy sectors.

This facilitates compliance with laws like LGPD (General Data Protection Law) and GDPR, while enabling experimentation and development with lower risk.

2. Greater agility in training AI models

Synthetic data allows rapid and controlled generation of large volumes of data, which accelerates the training of machine learning models, especially in scenarios where real data are scarce, incomplete, or unbalanced.

3. Simulation of rare or extreme scenarios

Real data do not always contain enough examples of exceptional cases or rare events. With synthetic data, it is possible to generate these scenarios tailored, improving the robustness of models and their ability to generalize.

Example: simulating financial fraud or critical failures in industrial systems, which are rare but impactful.

4. Reduction of operational costs and collection time

Collecting, cleaning, labeling, and validating real data can be costly and time-consuming. With synthetic data, it is possible to significantly reduce the time and cost associated with data preparation — and scale the base in a controlled manner.

5. Safer and more realistic testing environments

Synthetic data is ideal for creating controlled testing environments, without risks of exposing real data. This is especially useful in QA stages, system validation, or creating sandbox environments.

6. Facilitates collaboration between teams and partners

In projects involving multiple companies, suppliers, or distributed teams, sharing real data can be a challenge. Synthetic data enables this exchange without compromising confidentiality or information security.

What are the limitations and risks of using Synthetic Data?

Although the use of synthetic data offers various benefits, such as more privacy, scalability, and agility in AI projects, it is important to understand that this is not a magical solution. Like any technology, it brings challenges and limitations that must be considered to ensure real, ethical, and sustainable results.

Below, we list the main risks and precautions in using synthetic data:

1. Quality of synthetic data depends on the original base

If the real data used as a basis for generating synthetic data are incomplete, biased, or poorly structured, the artificially generated data will tend to reproduce (or even amplify) these distortions.

Risk: models trained with synthetic data may learn incorrect patterns or reinforce existing biases.

2. False sense of security regarding privacy

Although they do not contain real information, poorly generated synthetic data — especially those that attempt to replicate the original records too closely — may allow for indirect re-identification or expose sensitive structures.

Important precautions:

• Assess the similarity rate with real data;
• Use privacy validation techniques (like the Privacy Risk Score);
• Avoid overfitting during the generation process.

3. Limitations for extremely complex or contextual cases

Synthetic data work very well in tabular contexts or with predictable patterns. However, in highly contextual situations (like subjective human behavior or culturally-based decisions), the ability to simulate may still be limited.

Example: simulating behavioral nuances in legal decisions or deep human interactions in recommendation systems may require real data to complement training.

4. Lack of maturity in adoption

As a relatively new technology in the corporate market, there is still low maturity in some tools and processes related to the generation, governance, and validation of synthetic data.

This can lead to strategic errors such as:

• Generation of irrelevant data;
• Lack of traceability;
• Inadequate choice of tools or techniques.

5. Possible misalignment with production models

Models trained exclusively with synthetic data may perform well in the testing environment but do not necessarily replicate that performance in the real world — especially if there are variations in context, users, or integrations.

What tools or platforms allow generating quality Synthetic Data?

The generation of synthetic data can be done through different techniques — from statistical models to advanced artificial intelligence algorithms like GANs (Generative Adversarial Networks). However, to obtain high-quality, realistic, and secure data, it is essential to rely on specialized tools that offer precision, control, and governance.

Below are some of the main platforms and frameworks currently used to generate synthetic data reliably:

1. MOSTLY AI

One of the best-known platforms globally, MOSTLY AI uses AI to generate structured synthetic data while preserving statistical patterns and ensuring privacy.

Highlights:

• Generation of realistic synthetic data for regulated environments (finance, health);
• Accessible interface and easy integration with data systems;
• Has privacy validations and compliance with GDPR/LGPD.

2. Synthea

Designed for the healthcare sector, Synthea is an open-source tool that generates synthetic clinical data, simulating complete patient journeys.

Highlights:

• Ideal for testing hospital systems and developing AI in healthcare;
• Based on international interoperability standards (HL7, FHIR);
• Free and with an active community.

3. Gretel.ai

Gretel offers a data generation and anonymization platform focusing on machine learning and data security.

Highlights:

• Supports multiple formats (structured, text, real-time);
• APIs for direct integration into data pipelines;
• Quality evaluation and privacy risk assessment tools.

4. YData

A platform that combines synthetic data generation, quality profiling, and model development. Its differential is the focus on data-centric AI.

Highlights:

• Tools to mitigate bias and imbalance in datasets;
• Support for Python (SDK) and interactive notebooks;
• Heavily used to improve the performance of predictive models.

5. SDV (Synthetic Data Vault)

An open-source framework maintained by DataCebo and developed at MIT, ideal for technical teams that want total control over the generation process.

Highlights:

• Support for relational tables, time series data, and categorical data;
• Flexible, with native integration into Python environments;
• Widely used in POCs and academic projects.

How do Synthetic Data accelerate analytics and machine learning projects?

Analytics and machine learning projects essentially depend on a solid data foundation — and it is here that many initiatives face obstacles. Difficulties such as scarcity of real data, legal barriers, delays in collection, imbalanced or sensitive data often delay the evolution of models and limit experimentation.

Synthetic data emerge as a powerful solution to break these barriers and accelerate the end-to-end development cycle.

See how they boost data and AI projects in practice:

1. Reduce time for data preparation and collection

Generating custom synthetic data allows teams to initiate experiments and model validations without relying on the extensive collection or cleaning of real data — which are often time-consuming, costly, and bureaucratic.

Result: less time between problem definition and the first trained model.

2. Facilitate experimentation with different scenarios

By generating data with varied, unbalanced, or rare characteristics, synthetic data enhance the ability to simulate extreme or complex scenarios, improving the models’ generalization capacity.

Example: testing how a credit model behaves in the face of massive defaults or isolated frauds.

3. Eliminate legal and compliance barriers

In regulated sectors, using real data may require long approval cycles or anonymization. Synthetic data, not being linked to real individuals, allow projects to advance with more agility and less risk, as long as the generation follows privacy best practices.

4. Improve the quality and balance of datasets

Machine learning models perform better when trained with diverse and balanced data. Synthetic data help to correct imbalances in rare classes, filling critical gaps without “doubling” or distorting real data.

5. Favor collaboration between teams and partners

In many projects, sharing data between areas or companies is a bottleneck. With synthetic data, it is possible to share representative datasets without compromising confidentiality or security, facilitating co-creation and the advancement of models in collaborative environments.

Synthetic Data and AI projects

Artificial intelligence essentially relies on data — in sufficient volume, variety, and quality to feed algorithms and allow them to learn patterns, make decisions, and make predictions accurately. It is in this context that synthetic data come to the forefront, acting as an enabler for advancing AI projects with more agility, security, and control.

The relationship between synthetic data and AI goes beyond simply replacing real data: it is a strategy to overcome the main obstacles that currently limit the use of artificial intelligence at scale.

See how the two elements complement each other:

1. Synthetic data expand access to AI

Companies facing data restrictions — whether due to legal issues, sensitivity of information, or scarcity — often miss out on exploring AI-based solutions. With synthetic data, these barriers are removed, allowing more areas and teams to test and adopt AI with less friction.

2. Faster and more ethical model training

Synthetic data allow for training models without the risk of exposing personal or confidential data, reducing concerns about LGPD or GDPR. This enables the ethical use of AI from the outset, promoting what is called responsible AI.

3. Creation of richer and more representative datasets

With synthetic data, it is possible to simulate different audiences, behaviors, risk or exception situations — enriching the training data and resulting in more robust and fair models.

Example: avoiding bias in credit models by generating synthetic data representing groups that are less present in the real base.

4. Support for the entire AI lifecycle

From developing MVPs, through testing in secure environments, to monitoring and revalidation of models already in production, synthetic data offer flexibility and control at all stages of the AI lifecycle.

The use of synthetic data represents a turning point for companies seeking to explore the potential of artificial intelligence with greater speed, security, and governance. They allow overcoming operational limitations, expanding access to data, protecting privacy, and increasing the quality of machine learning models — all without compromising responsibility in the use of technology.

However, to reap these benefits, it is essential to have a mature technical approach aligned with the business reality.

At Objective, we combine our expertise in software engineering, data, and AI to help companies structure intelligent, reliable, and secure solutions, from strategy to delivery. We focus on responsible innovation, ensuring that data — whether real or synthetic — are treated ethically, accurately, and with a direct impact on results.

Want to know how your company can accelerate AI projects with synthetic data and reach new levels of efficiency and intelligence? Talk to our specialists and find out how Objective can be the ideal partner on this journey.