De-Regulate Your Regulated Data Webinar | Part 2

Many organizations have defined AI governance and data classification policies, but struggle with a critical question: where and how should AI data governance controls actually be enforced? As data moves across endpoints, mobile devices, networks, and cloud environments, sensitive information can be ingested into GenAI systems without visibility or intervention.

Join us to learn how to deploy AI data governance gatekeeper solutions across your IT infrastructure to maintain visibility and observability across AI dataflows, and what it takes to enforce AI data governance policies before data ever reaches a GenAI or LLM application.

Establishing an AI governance policy is only effective if you can enforce it in real-world environments.

Data leakage often occurs at endpoints, mobile devices, and network boundaries where regulated and confidential data is embedded in everyday workflows. Without a consistent gatekeeper layer, organizations lack control over where data flows, how it's prepared, and whether it should be permitted or denied for AI ingestion.

In this webinar, our experts will share insights into the practical implementation of AI data governance gatekeeper solutions. Live use cases will demonstrate how businesses can apply rule sets to de-regulate data and enforce governance in motion.

What You'll Learn:

Where AI data governance gatekeeper controls should be placed across endpoints, networks, cloud, and AI ingestion points.
How data classification policies translate into real-time monitoring and filtering decisions.
Where data leakage and data loss risks exist across enterprise dataflows.
How regulated and confidential data is de-regulated prior to GenAI or LLM ingestion.
How data lifecycle management (DLM) and privacy-by-design (PbD) are enforced through gatekeeper workflows.

Webinar Trilogy: De-Regulate Your Regulated Data

How Do You Implement an AI Data Governance Gatekeeper Solution?

What You'll Learn: