AI is Here. But Your Data Governance is Nowhere to be Found

Enterprise AI adoption has moved faster than almost anyone anticipated.

Copilots are being deployed across business functions. AI agents are being embedded into workflows. Teams are experimenting with generative AI platforms at every level of organisation.

But while AI capabilities have accelerated governance has struggled to keep pace. 

Many organisations are attempting to build AI-powered businesses on data estates that were never designed for this level of access, automation or exposure. Data inventories are incomplete. Classification programmes are inconsistent. Ownership is unclear. And governance models built for traditional analytics are being testing by a fundamentally difference operating environment.

The result is a growing gap between AI ambition and data readiness.

Join a select group of enterprise security, data, governance and risk leaders for a candid virtual roundtable exploring how data governance is evolving in the age of AI - and what organisations must do now to build a foundation capable of support AI at scale.

Discussion Topics:

1. What Does "AI-Ready Data" Actually Mean?

Many organisations talk about becoming AI-ready, but definitions vary widely. Discussion points include:

• How organisations define AI readiness
• Measuring data quality, accessability and trustworthiness
• Identifying governance gaps before AI initiatives scale
• Building data foundations that support long-term AI success

2. Do Traditional Classification Models Still Work?

Many classifications frameworks were built for compliance and security - not autonomous AI systems.

We'll discuss:

• Whether existing classification programmes remain fit for purpose
• Emerging approaches to AI-centric data governance
• Data context, sensitivity and usage controls
• Modernising governance for machine-driven environments

3. Managing Shadow AI Without Creating Resistance

Employees are already using AI tools whether organisations have formal policies in place or not.

Discussion topics include:

• Understanding the scale of shadow AI
• Encouraging responsible adoption
• Governance models that enable rather than restrict
• Balancing productivity with security and compliance

4. Third-Party AI Risk and Data Exposure

As organisations adopt external AI platforms and services, questions around data ownership and usage continue to grow.

We'll explore:

• Vendor due diligence for AI platforms
• Managing data used for model training and fine-tuning
• Contractual safeguards and governance controls
• Lessons learned from vendor evaluations and procurement decisions

5. Taking AI Data Risk to the Board

Boards increasingly recognise AI as a strategic opportunity - but often struggle to understand the associated risks.

Discussion areas include:

• Communicating AI governance risks in business terms
• Developing board-level reporting frameworks
• Aligning governance metrics with business outcomes
• Building executive confidence in AI programmes

The Future of AI Depends on the Data Beneath It

AI strategies are advancing rapidly. Data governance strategies are not.

The organisations that succeed with AI will not simply be those that deploy the most models. They will be the ones that build the visibility, controls and governance needed to use AI safely, responsibly and at scale.

Join fellow leaders for a candid discussion on what the next generation of data governance must look like - and how to build it before the gap becomes too large to close.

Request your invitation below. Attendance is limited to ensure an interactive, executive-level discussion.