Enterprise AI introduces new paths for sensitive data to be accessed, copied, transformed, and exposed. Information can move through training datasets, prompts, retrieval-augmented generation systems, vector databases, APIs, model outputs, application logs, and third-party AI services. Organizations cannot secure these environments effectively without first understanding what data they have, where it resides, who can access it, and how AI systems use it.

This session presents a practical framework for building an AI-ready data security program, beginning with data discovery, classification, ownership, and exposure assessment. It then explains how organizations can apply least-privilege access, encryption and centralized key management, masking, tokenization, data-loss prevention, secure retrieval controls, behavioral monitoring, and audit logging throughout the AI data lifecycle.

Attendees will also learn how to identify high-risk AI data flows, prioritize remediation based on business impact, establish collaboration among security, privacy, governance, data, and AI teams, and measure program effectiveness through meaningful security metrics. The session concludes with a phased implementation roadmap that organizations can adapt across on-premises, cloud, and hybrid environments. Participants will leave with an actionable blueprint for progressing from data visibility to continuous protection without slowing responsible AI innovation.