Generative AI has been generating a lot of hype, but most examples are seen as toys or broken. However, DataCebo is using generative algorithms in a practical and useful way. The company models enterprise data using generative AI and then uses those models to generate synthetic datasets with production-like qualities. Recently, DataCebo received $8.5 million in seed funding to further develop its vision. With this approach, customers can generate sample data that looks and behaves like real data but is not connected to the actual data, making it ideal for testing without compromising production data security.
Traditional testing methods involve manipulating live production data to remove sensitive information, but this can hinder proper testing of systems that rely on such data. DataCebo’s synthetic data offers a more efficient and effective solution by providing data that closely resembles real data. This allows for thorough testing of complex logic and algorithms, ensuring that new features work as intended without compromising sensitive information. This approach eliminates the need for creating custom test data generators for each system and simplifies the process of testing various data relationships.
DataCebo’s system automates tedious and time-consuming tasks involved in testing, allowing skilled data scientists to focus on more strategic initiatives. By reducing the amount of manual work required for testing, organizations can improve the quality and frequency of testing, enhancing security and robustness while lowering costs. This automated approach enables the deployment of more advanced testing techniques without the need for extensive customization or human intervention, making testing more efficient and reliable.
The use of generative AI in producing production-like test data is a rare yet promising application of the technology. While generative AI is often associated with creating realistic but false content, such as deepfake videos, DataCebo demonstrates how it can be used for practical and beneficial purposes in enterprise technology. By generating synthetic data that closely mimics real data, organizations can improve their testing processes and enhance security measures, ultimately leading to a more robust and secure system.
Testing is a crucial aspect of software development and cybersecurity, yet many organizations struggle to properly sanitize production data for testing purposes. DataCebo’s approach offers a solution to this challenge by providing realistic test data without compromising sensitive information, thereby reducing the risk of data breaches and ensuring thorough testing. With generative AI technology, organizations can enhance their testing processes, increase security measures, and lower costs, ultimately leading to a more reliable and secure system in the long run.
