This talk explores how Large Language Models can enhance Data Minimization practices compared to traditional methods. Advanced contextual understanding can accelerate data classification across an organization's storage locations, improve de-identification of text corpora, and streamline internal governance mechanics. The talk will propose architectures for combining LLM-based tools of various kinds with other techniques like lineage tracing to facilitate proactive data minimization and prevent data sprawl.
Charles de Bourcy is a Member of Technical Staff at OpenAI. He enjoys exploring new ways to improve privacy protections. He received his PhD from Stanford University.
