Enterprise AI systems are only as secure as the data that trains, grounds, and operates them. As organizations connect generative AI, machine learning platforms, retrieval-augmented generation systems, and AI agents to sensitive enterprise information, traditional perimeter-based controls are no longer sufficient.

This session presents a practical, technology-neutral blueprint for securing AI at the data layer across on-premises, cloud, and hybrid environments. It will examine common risks involving training datasets, prompts, model inputs and outputs, vector databases, data pipelines, APIs, and privileged access. Attendees will learn how data discovery and classification, least-privilege access, encryption and centralized key management, tokenization, masking, data lineage, behavioral monitoring, and audit controls can work together to protect sensitive information throughout the AI lifecycle.

The session will also introduce a phased implementation roadmap that helps security, data, and AI teams prioritize controls without slowing responsible AI adoption. Participants will leave with an actionable framework for reducing data exposure, strengthening governance, and building secure enterprise AI platforms.