As organizations invest in AI, many are discovering a new bottleneck: obtaining data that is both protected and useful. Engineering teams need realistic, production-like data to validate AI-generated changes, train models, test applications, and generate business insights. Yet, privacy initiatives can sometimes make that data harder to access, less representative of real-world conditions, or unable to preserve critical relationships between records. The Perforce Delphix “2026 State of AI and Data Privacy Report ” highlights this challenge. Among surveyed organizations, 26% say privacy...

Source: The New Stack