AI Is Here. But Your Data Governance Is Nowhere to Be Found.

AI Has Arrived. Has Your Data Governance Kept Up?

Enterprise AI adoption has outpaced the governance models designed to control it.

Copilots, autonomous agents, and AI platforms are accessing enterprise data estates that were never inventoried, classified, or scoped for this level of access. Meanwhile, the policies, organizational structures, and tools that defined the last decade of data governance were built for a fundamentally different risk landscape.

The result? Organizations are being asked to enable AI innovation while managing data risks they may not fully understand.

Gartner has projected that 60% of enterprise AI projects will be abandoned through 2026 because the data underneath them isn't ready. For the leaders accountable for that data, the pressure is mounting to rethink governance in real time - while AI is already in production.

The question is no longer whether your organization will adopt AI. It's whether your data governance can keep up.

Join a select group of enterprise security and data leaders for a candid, peer-driven virtual roundtable exploring how data governance is evolving in the age of AI.

The Governance Gap is Growing. The Questions Are Getting Harder.

Traditional data governance was not designed for a world where AI systems can retrieve, interpret, combine and act on enterprise information at unprecedented speed and scale.

As organizations accelerate AI adoption, leaders face a new set of challenges:

• Visibility: How can you govern data you haven't fully discovered, inventoried, or classified?
• Readiness: What does it actually mean for enterprise data to be ready of AI?
• Access: How do you ensure AI systems only access the information they should?
• Control: How do you manage employees using unsanctioned AI tools without undermining adoption?
• Accountability: Who owns AI data risk, and how do you make sure the board understands its implications?

These are not theoretical concerns. They are practical challenges facing organizations trying to balance innovation, security, compliance, and trust.

The next generation of data governance must do more than document policies. It must evolve alongside AI adoption, enabling organizations to understand their data, manage access, and make informed decisions about how information is used by AI systems.

Five Questions Every Data and Security Leader Should be Asking

This roundtable is built around the questions enterprise leaders are grappling with right now.

1. What Does AI-Ready Data Actually Mean? Every organization wants to make its data AI-ready. But what does that mean in practice? Explore how organizations define data readiness, assess data quality and accessibility, establish appropriate controls, and measure whether their data foundations can support enterprise AI.

2. Is Traditional Data Classification Enough? Classification programs were designed for a different era. AI introduces new ways for data to be retrieved, combined, inferred from, and exposed. Discuss whether existing classification frameworks can meet the demands of AI - or whether they need to be fundamentally rethought to account for AI-driven access and use.

3. How Do You Manage Shadow AI Without Killing Adoption? Employees are already experimenting with AI tools, sometimes outside approved environments and established policies. How can organizations understand and manage this activity, reduce unmanaged data exposure, and establish guardrails that enable responsible adoption rather than simply restricting it?

4. Who Controls Your Data Once It Enters a Third-Party AI Model?

Third-party AI providers introduce difficult questions about data retention, model training, fine-tuning and contractual control. Explore how organizations assess these risks, establish acceptable-use boundaries and decide when a vendor's data practices cross a line they are unwilling to accept.

5. Is Your Board Hearing the Real AI Data Risk Story? AI data risk can be traditionally complex, difficult to quantify and challenging to communicate in business terms. Discuss how leaders are translating governance gaps into business exposure communicating AI-related data risks to the board, and securing the accountability and investment needed to address them.

A Different Kind of Executive Roundtable

This is not another webinar filled with presentations and generic best practices.

This session brings together a small group of enterprise security and data leaders for an open, discussion-driven exchange focused on the realities of governing data in an AI-powered enterprise.

Expect candid perspectives, shared challenges, and practical lessons from peers navigating the same questions.

What You'll Gain:

Peer Perspectives - Understand how other organizations are approaching AI data governance, where they are encountering resistance, and what they are learning along the way.

Practical Insights - Identity governance practices, organizational approaches, and risk management strategies worth exploring within your own enterprise.

A Clearer View of Emerging Risks - Challenge established assumptions about data classification, AI readiness, shadow AI, third-party model usage, and executive accountability.

New Questions to Take Back to Your Organization - Leave with sharper questions, fresh perspectives, and a better understanding of where your governance approach may need to evolve.

Join the Conversation

AI is here. But your data governance is nowhere to be found.

Be part of a candid executive discussion on the future of data governance in the age of AI. Bring your perspective. Challenge conventional thinking. Learn how your peers are navigating the governance challenges that AI has made impossible to ignore. 

Register below.