Overview
As data-sharing models become more complex and AI-driven use cases continue to expand, traditional approaches to data de-identification are being reevaluated. Organizations are no longer operating within simple, linear disclosure frameworks – today’s environments often involve multiple recipients, overlapping roles, cloud-based infrastructures, and evolving downstream uses that challenge longstanding assumptions about privacy and confidentiality.
This webinar explored how de-identification strategies are adapting in response to these shifts, including the growing intersection with material non-public information (MNPI) considerations and the increasing scrutiny on how data is accessed, combined, and reused across ecosystems. Speakers discussed how organizations can assess and manage re-identification risk, structure data-sharing arrangements, and implement governance frameworks that remain effective in dynamic, multi-party environments.