Delphix Synthetic Data, a new offering from Perforce Software, a company that provides DevOps solutions specializing in AI governance for businesses, has been made available to the market. With this release, the first native AI solution has been brought to the market, which is capable of generating test data scenarios while maintaining strict referential integrity in interrelated enterprise systems.
Using automated discovery technologies, the software examines the data structure and business context in multi-database environments. Such an architecture makes it unnecessary to use manual setup required in traditional TDM software, thus providing immediate access to the datasets for engineers and AI agents regardless of whether production data is absent, insufficient, or prohibited due to privacy laws.
Bridging the Enterprise Synthetic Data Capability Gap
With the increase in the use of AI-assisted and agentic workflows, there has been an increased need for high-fidelity synthetic data, especially where testing edge cases and developing new features without the use of live production environments is required.
Traditional synthetic data generation techniques require manual configuration and are not very good at maintaining consistency in relational data. A 2026 industry study conducted by Perforce among 518 enterprise technology leaders highlighted a sharp disconnect between existing tools and organizational requirements:
- Only 34% of surveyed IT leaders reported that current synthetic data tools preserve referential integrity across systems.
- Only 36% confirmed that generated datasets provide the data realism required for rigorous testing and agentic workflows.
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Delphix Synthetic Data bridges this gap by using artificial intelligence to automatically parse metadata, evaluate schemas, and configure data models. By pairing statistical modeling with natural language prompting, teams can define, refine, and provision custom test datasets in minutes rather than waiting weeks for manual provisioning.
“Organizations need realistic test data that can be generated quickly, scale across complex environments, and meet data privacy requirements,” said Jim Mercer, Program Vice President, Software Development, DevOps, and DevSecOps, IDC. “Solutions like Delphix Synthetic Data that combine AI-driven automation with data quality and control are better positioned to address that need.”
“Your masked production data only tells you what already happened,” said Ilker Taskaya, Field CTO, Perforce Delphix. “Testing needs the cases that aren’t in production yet — and it needs them to hold together across every database and file format in the environment. Delphix Synthetic Data generates data in the shape teams specify, with referential integrity intact, so agentic development isn’t waiting on test data.”
Accelerating Quality, Speed, and Compliance
The solution addresses three core priorities for modern software engineering organizations:
- Accelerated Delivery Cycles: Streamlines pipeline velocity by offering on-demand provisioning of specialized test data for developers, automated CI/CD runs, and AI agentic workflows.
- Enhanced Software Quality: Delivers complex datasets that mimic the statistical distributions, variance, and relational context of real-world production environments.
- Proactive Risk Mitigation: Guarantees strict data compliance by removing sensitive personally identifiable information (PII) and real customer records from non-production environments.
Some of the key architectural characteristics include flexibility in using an “bring your own LLMs” approach, where sensitive telemetry does not leave the corporate environment, data masking at ingestion for the models, and automated schema mapping.
As part of the broader platform called the Delphix DevOps Data Platform, this synthetic data capability is complemented by automated data delivery, continuous masking, and centralized governance capabilities. Teams have the ability to access data through programmatic access using UIs, Enterprise APIs, and Model Context Protocol (MCP).
