Jeff Crume explains how AI agents, RAG pipelines, prompts, tools, and models move sensitive data through modern AI systems, and why traditional DLP falls short. He outlines a unified approach built on data lineage, workload and workforce visibility, proactive risk identification, and compliance reporting.
Daniel Chalef discusses how LLM synthesis destroys data provenance, making it difficult to trace information back to its sources. He introduces Graphiti, an open-source temporal graph framework, and Zep, an agent memory infrastructure built upon it. This system models provenance as a graph, treating sources as 'episodes' and derived facts as interconnected nodes and edges. This approach enables robust lineage tracing, handles entity merges and fact invalidation through temporal mechanisms, facilitates metadata-driven trust evaluation, and allows for compliant data deletion (e.g., GDPR) based on source support. The benefits include enhanced compliance, improved data veracity, and debuggable AI agents, addressing a critical engineering challenge in LLM applications.