Harnessing LLMs for Scalable Data Minimization

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.