Webinar Description
Key Takeaways
- Examines findings from the Perforce Delphix 2026 State of AI and Data Privacy Report
- Addresses the gap between perceived data protection confidence and actual compliance practices in AI/ML workflows
- Explores privacy risks specific to agentic development and machine learning pipelines
- Relevant for enterprise data leaders, compliance professionals and AI practitioners
Introduction
Delphix is hosting a webinar on 28 August 2026 examining the tension between rapid AI investment and data privacy compliance. The session draws on findings from the Perforce Delphix 2026 State of AI and Data Privacy Report to explore why enterprises remain vulnerable despite expressing high confidence in their data protection capabilities. With agentic AI development accelerating across industries, the webinar addresses a critical question facing technology and compliance leaders: how to maintain innovation velocity without compromising sensitive data.
The Confidence-Compliance Gap in AI Workflows
The Perforce Delphix 2026 State of AI and Data Privacy Report reveals a striking disconnect in enterprise data protection. According to the research, 98% of respondents express confidence in their ability to protect sensitive data within AI and machine learning workflows. However, 84% of these same organisations continue to permit data compliance exceptions in practice.
This fourteen-point gap between stated confidence and operational reality represents significant hidden risk. When organisations believe their data protection measures are adequate while simultaneously allowing exceptions to compliance policies, they create blind spots that can lead to regulatory violations, data breaches and reputational damage. The webinar will examine why this overconfidence persists and what structural factors contribute to the disconnect.
Privacy Challenges in the Age of Agentic Development
The session will address privacy concerns that are particularly acute for enterprises deploying AI and ML at scale. As organisations move beyond experimental AI projects into production systems—including agentic development where AI systems operate with greater autonomy—the volume and velocity of data processing increases substantially. Traditional compliance frameworks, designed for more static data environments, often struggle to keep pace.
Data quality emerges as a related challenge. Machine learning models require large volumes of representative data to function effectively, yet the pressure to feed these systems can conflict with data minimisation principles embedded in privacy regulations. Enterprises must balance the need for comprehensive training data against obligations to limit data collection and retention.
Balancing Innovation Speed with Data Protection
One of the central tensions the webinar will explore is how organisations can protect sensitive data without creating bottlenecks that slow AI innovation. Development teams under pressure to deliver AI capabilities quickly may view compliance processes as obstacles rather than safeguards. This dynamic can lead to the compliance exceptions identified in the report.
The Delphix experts presenting the session—Rod Cope, Mayank Ahluwalia and Matthew Yeh—will discuss approaches to embedding data protection into AI workflows in ways that support rather than impede development velocity. The goal is to demonstrate that compliance and innovation need not be opposing forces when data governance is architected thoughtfully from the outset.
Who Should Attend
This webinar is designed for enterprise professionals responsible for AI strategy, data governance and regulatory compliance. Chief data officers, privacy officers, AI and ML engineers, and security leaders will find the research findings and practical guidance directly applicable to their roles. The session is also relevant for technology executives evaluating how to scale AI initiatives while maintaining robust data protection standards.