Shadow Data, AI Pipelines, and the 802,000 Files You’re Oversharing Right Now

The average organization has more than 800,000 data files at risk from oversharing, erroneous access permissions, and inappropriate classification. That number is climbing as AI pipelines generate and ingest data faster than any manual classification effort can keep up. Half of all enterprise workloads are now cloud-based, and the rise of AI is accelerating data creation without guardrails or oversight. The result is shadow data: sensitive information scattered across environments that security teams cannot see, classify, or protect.

Traditional data security strategies assume that most data lives in known locations with defined access controls. That assumption broke years ago. Today, 90% of business-critical documents are shared outside the C-suite, AI models are training on datasets that may contain PII or intellectual property, and unstructured content is multiplying across SaaS, cloud storage, and collaboration platforms.

Regaining control requires visibility and coordination across data discovery, classification, access governance, and data protection controls – from DSPM and DLP to SaaS security and AI data governance.

Topics include:

• Discovering and classifying sensitive data across cloud, SaaS, and AI environments
• Addressing the shadow data problem created by AI-driven data proliferation
• Reducing oversharing risk through automated access governance and posture management

Join us to learn how organizations are regaining visibility and control over data they did not know they were exposing.

Relevant solution areas include: Data Security, Data Loss Prevention (DLP), Data Security Posture Management (DSPM), Cloud / Hybrid Cloud Security, Regulatory Compliance, Artificial Intelligence (AI), Governance, Risk & Compliance (GRC)