From Data Risk to AI Velocity: Building a Secure Private AI Foundation for Regulated Enterprise Growth
How can organizations unlock AI value while maintaining control, security, and trust across sensitive data environments?
AI is gaining traction across the enterprise, but progress is stalling where it matters most—data. With over 70% of enterprise data considered sensitive or regulated, many organizations struggle to scale AI without increasing risk or limiting access to the very data that drives value. As pressure builds to move beyond experimentation and deliver real outcomes, leaders are being forced to rethink how data is accessed, secured, and trusted across the AI lifecycle. So how can organizations move forward without slowing innovation or compromising control?
Leading organizations are shifting their approach, moving beyond isolated use cases to build a stronger data foundation for AI. This means connecting insights to trusted data, maintaining clear lineage, and embedding governance into how data is accessed and used. It also requires aligning strategy, infrastructure, and talent to ensure AI can scale in a way that is both effective and controlled.
Join peers and industry leaders for a candid discussion on how organizations are navigating the balance between data access, security, and AI innovation and what it takes to build a foundation that supports real, measurable impact.
Speakers &
Featured Attendees
Marco Carmona
Rameez Chatni
Discussion Topics
The challenge of building governance and oversight without slowing down access to critical data
How AI interacts with enterprise data across prompts, queries, and outputs—and where risk is introduced
What it takes to move from fragmented pilots to scalable AI built on a trusted data foundation
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