DataCebo Has Launched SDV 2.0 Software
The platform automates the creation of synthetic enterprise data for developers and testers.
Updated on Oct. 2, 2026 in Artificial Intelligence
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DataCebo has released SDV 2.0, a new software tool designed to generate synthetic relational data within a customer's own computing environment. The platform aims to reduce the operational burden of creating masked data copies for development while minimizing production data exposure.
Why it matters
The software enables companies in highly regulated sectors to maintain security by keeping sensitive information out of development environments. It automates complex tasks like schema detection and relationship identification, allowing technical teams to generate representative datasets efficiently.
SDV 2.0 runs on standard CPUs and supports major platforms like Oracle, SQL Server, BigQuery, Spanner, and AlloyDB. Pricing for the new enterprise software begins at USD $500 per month.
The players
DataCebo
A Cambridge-based company that developed the Synthetic Data Vault (SDV) framework.
ING Belgium
A financial institution that successfully generated 10,000 synthetic payments using the new software.
Epiconcept
A research organization that utilized the tool to construct a synthetic database in 55 minutes.
The details
By learning data relationships, structures, and constraints from a representative subset, the software automates the generation of synthetic databases. Clients like ING Belgium and Epiconcept have already leveraged the technology to create thousands of synthetic payments and databases in under an hour.
Timeline
DataCebo launched the SDV 2.0 software on October 2, 2026.
The Tech Race
The transition to version 2.0 represents a significant shift in how synthetic data is integrated into enterprise workflows compared to open-source prototypes. This evolution positions the company to compete more aggressively in the secure data generation market against traditional masking solutions.
Developers and data scientists can expect significant improvements in workflow efficiency through automated schema and constraint detection. The software also lowers barriers to entry for secure testing by allowing teams to operate directly within their existing computing environments.
The takeaway
Synthetic data tools are becoming essential for maintaining security and regulatory compliance in modern enterprise software development. Teams looking to optimize their testing processes should prioritize solutions that integrate seamlessly with existing cloud and on-premise databases.
Further reading
For more information on the evolving landscape of automated model generation, visit the Artificial Intelligence section.
Source note: This article includes information reported by SecurityBrief Asia.
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