Mastering Synthetic Data Generation with AI: Top Use Cases and Tools for 2025

Mastering synthetic data generation with AI is a critical focus in 2025, driven by increasing constraints on natural data availability and stringent privacy regulations. Synthetic data is transforming AI development by addressing privacy concerns, improving model accuracy, and enabling ethical innovation. As organizations grapple with data scarcity and the need for robust AI models, synthetic data emerges as a powerful solution, offering a way to generate vast amounts of realistic, yet artificial data that can be used for various applications without compromising privacy.

Understanding Synthetic Data

Synthetic data refers to artificially generated data that mimics real-world data in terms of statistical properties, patterns, and distributions. This data is created using advanced algorithms, often leveraging generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other machine learning techniques. The primary goal is to produce data that is indistinguishable from real data but does not contain any actual sensitive information, thereby mitigating privacy risks.

Types of Synthetic Data

Structured Data: This includes tabular data, such as databases, spreadsheets, and other organized formats. Synthetic structured data is commonly used in financial services, healthcare, and other industries where privacy is paramount.
- Example: A bank can generate synthetic customer transaction data to train fraud detection algorithms without exposing real customer information.
Unstructured Data: This encompasses images, videos, audio files, and text. Synthetic unstructured data is particularly useful in training AI models for computer vision, natural language processing, and other domains requiring large datasets.
- Example: An autonomous vehicle manufacturer can generate synthetic images of various driving scenarios to train its AI models to recognize and respond to different road conditions.
Semi-Structured Data: This type of data combines elements of both structured and unstructured data, such as JSON files, XML documents, and NoSQL databases. Synthetic semi-structured data is used in various applications, including web development and data analytics.
- Example: A healthcare provider can generate synthetic patient records in JSON format to train AI models for diagnosing diseases without compromising patient privacy.

Top Use Cases for Synthetic Data in 2025

AI Model Training

One of the most significant use cases for synthetic data is in AI model training. Traditional AI development relies heavily on real-world data, which can be scarce, expensive to acquire, or subject to strict privacy regulations. Synthetic data offers a solution by providing perfectly labeled and annotated datasets that can be used to train AI models without compromising privacy.

Healthcare Industry

In the healthcare industry, synthetic data can be used to train AI models for diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. By generating synthetic patient records, healthcare providers can ensure that AI models are trained on diverse and representative datasets without exposing sensitive patient information.

Example: A hospital can generate synthetic patient data, including medical histories, lab results, and imaging data, to train an AI model for early detection of cancer. The synthetic data can mimic the statistical properties of real patient data, enabling the AI model to learn patterns and features associated with different types of cancer without accessing actual patient records.

Financial Services

In the financial services industry, synthetic data can be used to train AI models for fraud detection, credit scoring, and risk assessment. By generating synthetic transaction data, financial institutions can ensure that AI models are trained on diverse and representative datasets without exposing sensitive customer information.

Example: A bank can generate synthetic transaction data that mimics real-world patterns, including fraudulent transactions, to train an AI model for fraud detection. The synthetic data can help the AI model learn to identify patterns and anomalies associated with fraudulent activities, enabling the bank to detect and prevent fraud more effectively.

Software Testing

The demand for synthetic data in software testing is rising sharply, as it enables testing with realistic but non-sensitive data, minimizing privacy risks. Software developers can use synthetic data to test the functionality, performance, and security of their applications without worrying about data breaches or compliance issues.

E-Commerce Platforms

In the e-commerce industry, synthetic data can be used to test the functionality and performance of online platforms. By generating synthetic user data, including browsing behavior, purchase history, and demographic information, e-commerce companies can ensure that their platforms are robust and scalable without exposing real customer data.

Example: An e-commerce company can generate synthetic user data to test the performance of its website under high traffic conditions. The synthetic data can simulate the behavior of real users, including browsing products, adding items to carts, and making purchases, enabling the company to identify and address performance bottlenecks before launching the platform to real users.

Financial Applications

In the financial industry, synthetic data can be used to test the functionality and security of financial applications. By generating synthetic transaction data, financial institutions can ensure that their applications are secure and compliant with regulations without exposing real customer data.

Example: A financial institution can generate synthetic transaction data to test the security of its online banking platform. The synthetic data can simulate various types of transactions, including transfers, payments, and withdrawals, enabling the institution to identify and address security vulnerabilities before exposing real customer data to potential risks.

Image and Video AI Models

Synthetic data is predicted to comprise over 95% of training data for image and video AI models by 2030, indicating its dominant role in computer vision. Synthetic images and videos can be generated using generative models such as GANs, which can create realistic visual data for various applications, including autonomous vehicles, surveillance, and augmented reality.

Autonomous Vehicles

In the autonomous vehicle industry, synthetic data can be used to train AI models for object detection, lane keeping, and decision-making. By generating synthetic images and videos of various driving scenarios, autonomous vehicle manufacturers can ensure that their AI models are well-prepared for real-world conditions without relying on actual driving data.

Example: An autonomous vehicle manufacturer can generate synthetic images of different road conditions, including rain, snow, and fog, to train an AI model for object detection. The synthetic data can help the AI model learn to recognize and respond to various obstacles, such as pedestrians, cyclists, and other vehicles, enabling the manufacturer to develop safer and more reliable autonomous vehicles.

Surveillance Systems

In the surveillance industry, synthetic data can be used to train AI models for object detection, facial recognition, and behavior analysis. By generating synthetic images and videos of various scenarios, surveillance system providers can ensure that their AI models are accurate and reliable without compromising privacy.

Example: A surveillance system provider can generate synthetic images of different environments, including airports, shopping malls, and public transportation hubs, to train an AI model for facial recognition. The synthetic data can help the AI model learn to recognize and identify individuals accurately, enabling the provider to develop more effective and privacy-preserving surveillance systems.

Edge Scenario Simulation

Synthetic data usage for training AI in edge cases—rare or unusual scenarios—is expected to grow from 5% today to over 90% by 2030, helping AI systems manage complex real-world conditions. Edge scenarios refer to situations that are infrequent but critical, such as rare medical conditions, extreme weather events, or unusual financial transactions.

Healthcare Industry

In the healthcare industry, synthetic data can be used to train AI models for diagnosing rare diseases, predicting patient outcomes, and personalizing treatment plans. By generating synthetic patient data that simulates rare medical conditions, healthcare providers can ensure that their AI models are robust and accurate without relying on actual patient data.

Example: A healthcare provider can generate synthetic patient data that simulates rare medical conditions, such as rare cancers or genetic disorders, to train an AI model for diagnosis. The synthetic data can help the AI model learn to recognize and respond to rare conditions, enabling the provider to develop more accurate and reliable diagnostic tools.

Financial Services

In the financial services industry, synthetic data can be used to train AI models for detecting and responding to rare and sophisticated cyber threats. By generating synthetic data that simulates various attack scenarios, financial institutions can ensure that their AI models are robust and capable of handling real-world threats.

Example: A financial institution can generate synthetic data that simulates rare and sophisticated cyber threats, such as advanced persistent threats (APTs) or zero-day exploits, to train an AI model for threat detection. The synthetic data can help the AI model learn to recognize and respond to rare threats, enabling the institution to develop more effective and resilient cybersecurity systems.

Federated Learning

Synthetic data supports federated learning approaches, allowing multiple AI models to be trained collaboratively without sharing private information, thus preserving data confidentiality. Federated learning is a decentralized approach to AI training, where multiple parties can train a shared model without exchanging raw data.

Healthcare Industry

In the healthcare industry, synthetic data can be used to support federated learning approaches for training AI models for diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. By generating synthetic patient data, healthcare providers can collaborate on training shared AI models without sharing actual patient records.

Example: A consortium of hospitals can generate synthetic patient data to train a shared AI model for diagnosing rare diseases. The synthetic data can help the AI model learn to recognize and respond to rare conditions, enabling the hospitals to collaborate on developing more accurate and reliable diagnostic tools without compromising patient privacy.

Financial Services

In the financial services industry, synthetic data can be used to support federated learning approaches for training AI models for fraud detection, credit scoring, and risk assessment. By generating synthetic transaction data, financial institutions can collaborate on training shared AI models without sharing actual customer data.

Example: A consortium of banks can generate synthetic transaction data to train a shared AI model for fraud detection. The synthetic data can help the AI model learn to recognize and respond to various types of fraud, enabling the banks to collaborate on developing more effective and resilient fraud detection systems without compromising customer privacy.

Key Challenges

Despite the numerous benefits of synthetic data, there are several challenges that organizations need to address:

Quality of Synthetic Data: The quality of synthetic data can sometimes fall short of matching real data, potentially leading to AI errors. Ensuring that synthetic data is realistic and representative of real-world data is crucial for the success of AI models.
- Example: In the healthcare industry, synthetic patient data must accurately mimic real patient data to train AI models for diagnosing diseases. If the synthetic data is not representative of real patient data, the AI model may not perform well in real-world scenarios.
Privacy Concerns: Synthetic data might inadvertently contain privacy-leaking clues if not carefully generated. Organizations need to ensure that synthetic data is generated in a way that does not compromise privacy.
- Example: In the financial services industry, synthetic transaction data must not contain any identifiable information that could be linked back to real customers. If the synthetic data contains privacy-leaking clues, it could lead to data breaches or compliance issues.
Regulatory Compliance: Evolving privacy regulations require continuous compliance efforts from organizations using synthetic data. Staying up-to-date with regulatory requirements and ensuring compliance is essential for organizations to avoid legal and financial risks.
- Example: In the healthcare industry, organizations must comply with regulations such as HIPAA and GDPR when generating and using synthetic patient data. Failure to comply with these regulations could result in legal and financial penalties.

Despite these challenges, technological advancements are rapidly improving synthetic data quality and addressing privacy concerns. Organizations are investing in advanced generative models, data augmentation techniques, and privacy-preserving algorithms to enhance the quality and reliability of synthetic data.

Leading Synthetic Data Generation Tools for 2025

The market for synthetic data generation tools is growing, with several top platforms providing advanced capabilities to create secure, realistic fake data. Notable tools include:

K2view: K2view offers a comprehensive data platform that enables organizations to generate synthetic data for various use cases, including AI training, software testing, and data analytics. The platform leverages advanced generative models to create realistic and privacy-preserving synthetic data.
- Example: A financial institution can use K2view to generate synthetic transaction data for training AI models for fraud detection. The platform can create realistic and diverse synthetic datasets that mimic real-world transaction patterns, enabling the institution to develop more accurate and reliable fraud detection systems.
Gretel: Gretel provides a suite of tools for generating synthetic data, including Gretel Synthetics, which uses generative models to create synthetic datasets. The platform is designed to ensure data privacy and compliance with regulations such as GDPR and HIPAA.
- Example: A healthcare provider can use Gretel to generate synthetic patient data for training AI models for diagnosing diseases. The platform can create realistic and diverse synthetic datasets that mimic real patient data, enabling the provider to develop more accurate and reliable diagnostic tools without compromising patient privacy.
MOSTLY AI: MOSTLY AI offers a synthetic data generation platform that leverages AI to create realistic and privacy-preserving synthetic data. The platform is used by organizations in various industries, including finance, healthcare, and retail, to train AI models and test software applications.
- Example: An e-commerce company can use MOSTLY AI to generate synthetic user data for testing the functionality and performance of its online platform. The platform can create realistic and diverse synthetic datasets that mimic real user behavior, enabling the company to identify and address performance bottlenecks before launching the platform to real users.
Syntho: Syntho provides a synthetic data generation platform that uses generative models to create realistic and privacy-preserving synthetic data. The platform is designed to support a wide range of use cases, including AI training, software testing, and data analytics.
- Example: An autonomous vehicle manufacturer can use Syntho to generate synthetic images and videos of various driving scenarios for training AI models for object detection. The platform can create realistic and diverse synthetic datasets that mimic real-world driving conditions, enabling the manufacturer to develop safer and more reliable autonomous vehicles.
YData: YData offers a synthetic data generation platform that leverages AI to create realistic and privacy-preserving synthetic data. The platform is used by organizations to train AI models, test software applications, and conduct data analytics.
- Example: A surveillance system provider can use YData to generate synthetic images and videos of various environments for training AI models for facial recognition. The platform can create realistic and diverse synthetic datasets that mimic real-world environments, enabling the provider to develop more effective and privacy-preserving surveillance systems.

These tools leverage generative AI to produce synthetic data tailored for various domains such as structured data, image, and video data, supporting the scaling of AI training and software testing needs. Organizations can choose the tool that best fits their requirements and integrate it into their data pipelines to generate high-quality synthetic data.

Industry Trends

Several industry trends are shaping the future of synthetic data generation:

Data Access Restrictions: Natural data availability is tightening due to data access restrictions by companies and model makers, prompting a shift toward synthetic data usage. Organizations are increasingly turning to synthetic data to overcome data scarcity and privacy concerns.
- Example: In the healthcare industry, data access restrictions imposed by regulations such as HIPAA and GDPR are limiting the availability of real patient data for AI training. Organizations are turning to synthetic data to overcome these restrictions and develop more accurate and reliable AI models.
Synthetic Training Methods: Synthetic data and synthetic training methods are rapidly expanding, allowing AI intelligence to scale even as natural data sources become limited. Advanced generative models and data augmentation techniques are enabling organizations to create realistic and diverse synthetic datasets for AI training.
- Example: In the autonomous vehicle industry, synthetic data is being used to train AI models for object detection, lane keeping, and decision-making. Advanced generative models are enabling organizations to create realistic and diverse synthetic datasets that mimic real-world driving conditions, allowing AI models to scale and improve performance.
Market Growth: Gartner forecasts synthetic structured data will grow at least three times faster than real structured data for AI training through 2030. The market for synthetic data generation tools is expected to grow significantly, driven by the increasing demand for privacy-preserving and scalable AI training solutions.
- Example: The market for synthetic data generation tools is expected to grow rapidly, with organizations investing in advanced platforms to create realistic and diverse synthetic datasets for AI training. This growth is driven by the increasing demand for privacy-preserving and scalable AI training solutions across various industries.
Privacy Violation Sanctions: By 2030, synthetic data is expected to help companies reduce privacy violation sanctions by 70% by decreasing reliance on personal customer data. Organizations can use synthetic data to train AI models and test software applications without exposing sensitive customer information, thereby minimizing the risk of privacy violations.
- Example: In the financial services industry, organizations can use synthetic data to train AI models for fraud detection and risk assessment without exposing sensitive customer information. This reduces the risk of privacy violations and helps organizations comply with regulations such as GDPR and CCPA.

In summary, mastering synthetic data generation is paramount in 2025 to fuel AI development securely and efficiently. The growing ecosystem of sophisticated tools and expanding use cases underscores synthetic data's integral role in shaping the future of AI across industries without compromising privacy and compliance. As organizations continue to invest in synthetic data generation technologies, the potential for AI innovation and ethical data usage will only expand, paving the way for a more secure and privacy-preserving digital future.