Ensuring Data Integrity in IoT & AI: A Provenance-Based Approach

Securing data from embedded and industrial IoT systems is challenging due to security gaps that may be present in existing protocols and complex data pipelines. These issues are likely to persist as standards evolve slowly. Key challenges include protecting data at its origin, ensuring security without sacrificing interoperability, and maintaining data integrity across various system layers throughout its lifecycle, especially in IoT and AI applications. This presentation will explore the challenges of securing data from embedded devices in IoT and AI applications, discuss historical cybersecurity incidents, and review emerging regulations. Additionally, it will provide insights into the implementation and benefits of implementing data provenance.