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#ai #security
As organizations integrate Generative AI into their systems, securing data access for both human users and AI agents has become a critical challenge. Traditional access control approaches fall short when AI systems need contextual, document-level permissions at scale and speed.
This talk demonstrates how Fine-Grained Authorization (FGA) provides robust security for Retrieval-Augmented Generation (RAG) and agentic AI systems. Learn how to implement permission models that protect sensitive information while enabling AI to access only authorized data.
The talk explores implementations using OpenFGA and LangChain, showcasing how to build security directly into AI retrieval pipelines.
The presenters will provide real world case studies to discover how enterprises can prevent data leakage, implement multi-tenant isolation, and maintain audit trails while scaling to billions of access decisions.
Open source tools like OpenFGA and integration techniques with vector databases will be featured, along with best practices for real-world deployment. Thus join us to understand how one can maintain security without sacrificing performance or user experience in Agentic / Gen AI applications.
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