AI systems cannot be trustworthy when the data supporting them is unclassified, overexposed, poorly governed, or inadequately protected. As enterprises adopt generative AI, retrieval-augmented generation, vector databases, machine-learning pipelines, and AI agents, sensitive information may flow through prompts, embeddings, model outputs, application logs, APIs, and third-party platforms.

This session presents a practical, data-first blueprint for building trustworthy AI by design. Attendees will learn how to protect information throughout the AI lifecycle by using data discovery and classification, minimization, lineage, retention, least-privilege access, encryption and centralized key management, masking, tokenization, continuous monitoring, and audit controls.

The presentation will examine common enterprise risks, including unauthorized training data, excessive permissions, insecure data pipelines, sensitive-data exposure through AI responses, weak retrieval controls, and inadequate third-party governance. It will end with a phased implementation model that security, privacy, data, and AI teams can use to go from the first risk assessment to controls that can be enforced and assurance that can be measured. Participants will leave with an actionable checklist for enabling responsible AI innovation without compromising data security or privacy.