MIND Research Links Data Trust to AI Performance and Risk Management for CISOs

New updates have been reported about MIND.

MIND has released new research, produced with the CISO Executive Network, showing that weak data security and governance are undermining enterprise AI performance even as adoption surges. The study, titled “The Impact of Data Trust on AI Initiative Success,” finds that about 90% of organizations are running generative AI at scale, yet only 20% of initiatives hit their key performance targets and 65% of CISOs lack confidence in existing data security controls.

MIND positions data trust—the confidence that AI and other systems will use data safely and appropriately—as a core determinant of AI speed, value, and risk. Co-founder and CEO Eran Barak warns that AI has moved into full-scale production without the necessary data foundations, creating a structural gap between rapid deployment and adequate control that can stall projects or introduce material risk.

Based on a survey of 124 CISOs and 20 in-depth interviews, the research highlights that many organizations have AI policies on paper but cannot enforce them at machine speed, operate with largely unclassified and ungoverned data estates, and rely on security frameworks designed for human users rather than autonomous systems. This mismatch is driving concrete failures, including stalled AI rollouts, compliance exposure, and potential business disruption, rather than merely hypothetical threats.

Nearly two thirds of CISOs report low confidence in their ability to stop unsafe AI access to sensitive data, even as business leaders push to accelerate AI deployment for competitive advantage. Bill Sieglein, founder and COO of the CISO Executive Network, notes that data trust is emerging as a key differentiator between organizations that scale AI safely and those that are forced to slow or reconsider their programs.

MIND’s analysis frames AI as a stress test of existing security fundamentals and argues that robust data foundations convert security from a defensive obligation into a business enabler. The company is positioning its autonomous data security platform as a way to discover and classify sensitive data, remediate security issues, and prevent data leaks in real time, effectively putting data loss prevention and insider risk management on autopilot.

By enabling what it calls “Stress-Free DLP,” MIND aims to let enterprises align AI velocity with risk controls so that security operates at the same pace and precision as AI workloads. For executives, the findings underscore that AI competitiveness increasingly depends on measurable data trust, not just model capability, and that investment in modern data security architectures is becoming a prerequisite for scaling AI initiatives without incurring outsized regulatory, operational, or reputational risk.