Perforce Software announced a major expansion of its software testing and synthetic data offerings with the introduction of AI-powered synthetic data generation.
Delphix AI introduces a new language model embedded into the Delphix DevOps Data Platform, allowing teams to automatically deliver synthetic data across the development lifecycle.
Perforce introduces Delphix AI, powered by Perforce Intelligence, to create AI-generated synthetic data for fast, secure application delivery.
Uniting data masking, data delivery, and synthetic data generation in a single platform, and boosting it with AI, Delphix provides an enterprise-grade solution for DevOps acceleration, privacy compliance, and AI/ML model training – a clear advantage over point tools built for narrower use cases.
According to Gartner, “by 2027, 60% of data and analytics leaders will face critical failures in managing synthetic data, risking AI governance, model accuracy, and compliance.” The new capability from Perforce addresses this challenge. With the latest release, data and analytics as well as development teams can leverage a built-in, optional AI model trained on Delphix’s extensive IP in data privacy. The result is enterprise-grade synthetic data – customisable, referentially intact, and ready for use in compliant, high-fidelity test environments.
“As organisations modernise software development and adopt AI, intelligent test data management becomes a strategic enabler of innovation and compliance,” said Jim Mercer, Program Vice President, Software Development, DevOps & DevSecOps at IDC. “Perforce Delphix’s AI-powered capabilities are well-timed to help customers meet evolving challenges in this space.”
“From a best-practices standpoint, masked data preserves the structure of sensitive values while AI-powered synthetic data contributes safe, compliant data for new scenarios that may not exist in production,” said Robert Henning, Lead Technical Staff, Enterprise Data, Athenahealth. “Together, they empower organisations in regulated industries, such as healthcare, to protect privacy while still enabling robust testing, analytics, and innovation.”
AI-powered compliance for modernised, secure testing
While other companies rely on legacy tools or manual processes that bottleneck application delivery, analytics, and AI initiatives, Delphix ensures developers, testers, data scientists always have access to compliant data.
Companies should not have to compromise on speed, security, and compliance. Perforce’s approach to AI-generated synthetic data ensures organisations can:
- Protect sensitive data by replacing it with synthetic data that is tailored to unique functional needs and scenarios, such as industry and geography, while retaining realism for dev/test environments
- Accelerate data privacy compliance and security across key large-scale use cases like analytics, AI model training, and application development
- Drive secure, compliant, and responsible AI-focused innovation at business speed – without investing in additional data science resources and while keeping data safe within their environment
Perforce Intelligence: a foundation for AI in data management
Perforce Intelligence, Perforce’s blueprint for embedding AI across its products, is behind this new capability and will be a core part of the larger AI-powered synthetic data generation initiative to come.
“We’re making AI available where it’s historically been forbidden,” said Steve Karam, Principal Product Manager at Perforce. “With a fully air-gapped, in-platform small language model running on CPU only, organisations generate synthetic data inside their IT perimeter, thus harnessing AI responsibly, securely, and cost-effectively.”
Additional intelligent data compliance and test data management capabilities will follow in upcoming releases, including unstructured data discovery and automated data delivery for MLOps pipelines.
Perforce is committed to expanding intelligent automation and insights across the data lifecycle to support high-performance engineering at scale. The company has a strong focus on transforming what AI can do within the testing space, which is one of the main blockers in DevOps efficiency and the automation of application delivery.
