For many organizations, the concept of data provenance remains an untapped area of focus, yet it underpins every aspect of trust, compliance, and operational risk. As Kubernetes continues to drive innovation in scalable data platforms and AI systems, gaps in data traceability and origin verification introduce risks that many teams are unaware of until they manifest as failures, inefficiencies, or regulatory noncompliance.

This talk will uncover why data provenance is critical to modern AI and containerized systems, particularly within Kubernetes ecosystems. We’ll explore real-world scenarios where missing or unclear data origins led to compounding risks, and how understanding provenance can mitigate cascading impacts on AI models, synthetic data use, and compliance efforts.

Attendees will leave with a clear understanding of the importance of provenance in managing data and risk. We’ll discuss simple, actionable steps to introduce provenance into Kubernetes workflows, enabling teams to better track, validate, and secure their data. By highlighting practical examples and emerging trends, this session will empower participants to view provenance not as an abstract concept, but as a critical enabler of trust and resilience in their AI and data systems.