Webinar Trilogy: De-Regulate Your Regulated Data
Part 3: How to Operationalize AI Data Governance with People, Process, & Technology
Many organizations have already defined their AI governance and AI data governance frameworks, but struggle to move from policy to execution. Policies alone don’t ensure compliance if the workforce isn’t trained, processes aren’t aligned, and technology isn’t providing continuous visibility. Without readiness across these areas, even well-designed governance models fail to scale.
Join this webinar to learn how to move from intent to execution by operationalizing your AI data governance controls. This session focuses on workforce readiness, data governance workflows, and technology enablement required to ensure AI data governance is consistently applied across public and private GenAI and LLM use.
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Struggling to Operationalize Your AI Data Governance?
Defining AI data governance frameworks is only the first step. Organizations often struggle with execution – ensuring your employees follow acceptable use policies, processes classify and prepare data for AI ingestion, and technology provides visibility, observability, and enforcement.
In this webinar, you'll learn how to operationalize AI data governance by aligning your people, processes, and technology. Join us for practical insights into how AI data governance gatekeeper functions work in real-world environments and how to ensure data is properly identified, classified, cleansed, and approved before entering public or private GenAI systems.
What You'll Learn:
- What workforce readiness looks like for AI data governance, including acceptable use, training, and awareness.
- How AI data governance gatekeeper workflows identify, classify, prepare, and cleanse data prior to AI ingestion.
- Where data lifecycle management and privacy-by-design controls are operationalized in AI workflows.
- How technology enables visibility, observability, and continuous monitoring of AI data use.