Data-First Security Strategies for Enterprise AI

Enterprise AI is already running at scale, but data security gaps are limiting how far it can go. Only 35% of organizations have full visibility into where their unstructured data resides, 23% cannot scan it for risk at all, and once AI systems start accessing that data, visibility can break down fast.

This report captures four requirements for secure data foundations and safe AI at scale, drawn from conversations with leaders at Citi, Google Cloud, and Veeam.

Inside the report:

• How to map AI access to sensitive data, classify unstructured content at the source, set AI access boundaries, and surface shadow AI already processing sensitive data
• What a shared definition of AI-ready data looks like in practice, including automated classification before training and access controls embedded in model operations rather than bolted on
• What human and machine identities can access your data and AI systems, and how to restrict that access with deterministic controls

The institutions getting this right are building secure data foundations first, so AI-ready data is the default, rather than the exception. See how they do it. Download now.