Perforce's Latest Survey Finds Confidence vs. Reality Gap with Data Protection

Perforce Software announced the release of its third annual State of Data Compliance and Security Report. Based on a global survey of more than 500 enterprise leaders conducted in partnership with Hanover Research, the report reveals a disconnect between how confident organizations feel about protecting sensitive data and the risks they are exposed to, like data breaches and audit failures. The findings reflect the perspectives of leaders at large enterprises, primarily ones generating more than $1 billion in annual revenue and employing 25,000 or more people. The report reveals that while data masking policies are widely adopted, they are not consistently enforced. 99% of organizations have data masking mandates in place, yet 84% still allow compliance exceptions. Further, 98% of survey respondents report confidence in their ability to protect sensitive data. However, organizations continue to face persistent risks, barriers, and compliance gaps: 77% are concerned about data breaches or theft in non-production environments. 74% are concerned about audit failures. Most alarmingly, 43% have already experienced audit failures, and 34% experienced breaches or theft. "We're seeing a contradiction in the data: strong confidence despite concerns and real-world risks," said Ross Millenacker, Senior Product Manager for Perforce Delphix and one of the report's authors. "That tension highlights how challenging it is to protect sensitive data at scale without the right solutions, particularly as organizations expand into agentic development." The same confidence vs. reality gap is playing out in AI. 86% of organizations have AI data privacy mandates and 98% express confidence in protecting sensitive data in AI workflows, yet 68% are concerned about data leaks and 62% about training data breaches in these environments. Organizations are increasing investments in AI data protection, with 80% planning to invest in solutions that protect sensitive data used for AI/ML model training and fine-tuning in 2026-2027. Data quality is the #1 barrier to protecting sensitive data. 51% of leaders cite data quality as the biggest challenge in protecting data in AI, ML, and analytics workflows. The AI boom is driving a new data protection priority: Databricks and Snowflake. Enterprises now rank leading analytics platforms Databricks and Snowflake among the top sources requiring masking, reflecting the rising stakes of securing the data that powers AI innovation.