Bill Schaumann, Priyadarshi Prasad
AI modules trained only on SAAS-based external data sources pose a privacy challenge, and significantly limit control over an organization’s data, data use cases, and company-specific challenges. Instead of a centralized machine learning training ground, advanced concepts in AI deployment may incorporate a way to have millions of self-learning AI modules, each of which learns within their organization and adheres to rules and boundaries. This learning concept would help keep privacy concerns visible and managed, and also increase the relevancy of AI output, creating more effective control layers at the local level. Every organization can put guard rails on AI learning as they deem fit, aligned with the local and global privacy requirements, and still meet their business needs.
Join this session for a discussion and a practical demonstration of locally trained AI models that can help privacy engineers bring a competitive advantage to their organizations using AI with proper guard rails and eliminate any privacy risks.
Priyadarshi Prasad, Co-Founder, Lightbeam
Bill Schauman, Independent Privacy Consultant, Practical Privacy LLC
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