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 controls make production-quality data harder to obtain, 25% struggle to preserve relationships across data entities, and 51% cite data quality challenges. 

“Protecting data isn’t enough if it can no longer support the systems that depend on it.”

Protecting data isn’t enough if it can no longer support the systems that depend on it. For engineering teams, the question is whether their data protection strategies can preserve the qualities that make data valuable in the first place. You need a well-rounded data strategy with tools that maintain referential integrity and relationships across your environments.

At first glance, statistics from the report, like “51% of enterprises cite data quality challenges,” may sound like a purely governance issue.

“When an AI model is trained on incomplete or distorted data, its outputs become less reliable.”

When an AI model is trained on incomplete or distorted data, its outputs become less reliable. When test environments contain unrealistic data, defects can escape into production. When analytics datasets lack consistency, teams spend more time validating results than acting on them.

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