When Data Types Collide: Governing Privacy Risk for Multimodal Data

This educational panel will examine how privacy teams can manage risk as organizations combine structured data, free text, images, audio, biometrics and behavioral signals in shared analytics environments. Panelists will move beyond theory to explain how identifiability risks emerge across modalities, why traditional anonymization controls may fall short, and how governance can scale without blocking responsible data use. Attendees will leave with practical approaches for assessing cross-modal linkability, applying consistent risk-scoring frameworks, selecting privacy-enhancing technologies, and aligning data environment, access, and oversight controls with regulatory expectations and enterprise risk tolerance.

What you will learn:

• Understand how re-identification and model leakage risks can emerge across data sources, especially when datasets are linked or used together.
• Learn practical approaches for anonymization and privacy risk management in multi-modal environments, including modality-aware techniques and consistent risk-scoring frameworks.
• Gain actionable strategies for embedding governance, accountability and regulatory alignment across the data lifecycle while enabling responsible innovation and data utility.

Sponsored by Privacy Analytics, an IQVIA company