As AI initiatives scale, data access requests no longer arrive in controlled waves—they happen continuously, at machine speed. Legacy RBAC models and ticket-driven approvals were built for human workflows, not autonomous agents. When AI runs at machine speed but access architecture operates at human speed, operational breakdown is inevitable.

In this on-demand panel discussion, enterprise data leaders examine where traditional access models fracture under AI demand, what “machine-speed provisioning” really looks like in practice, and how to modernize infrastructure before projects stall, audit trails blur, or exposure risk grows.

Key Insights Covered:

• Identifying Critical Bottlenecks: Clear signals that traditional access models fail to scale with AI demand.
• Modern Frameworks: A blueprint for implementing policy-driven provisioning.
• Actionable Strategies: Practical steps to transition from manual ticket workflows to real-time enforcement.
• Executive Alignment: How CDOs and CISOs can align access modernization with broader AI infrastructure goals.

Featured Speakers

• Kiran Kanetkar, GenAI Executive Insights at Cencora
• Della Shea, Head of Enterprise Risk at Interac Corp
• Rajesh Arora, VP, CDAO at Principal Financial Group
• Moderated by: Paul Myres, Field CTO at Immuta

Core Discussion Themes

Our panel of experts addressed the critical questions facing data leaders today:

• Where Legacy Models Break: What makes AI-driven data demand fundamentally different from traditional BI or analytics expansion?
• Defining “Machine-Speed Provisioning”: What decisions can realistically be automated, and where must human oversight remain?
• Moving Beyond the Ticket: What a modern, policy-driven provisioning framework consists of, and how to take the first step this year.
• Continuous Control: How audit, compliance, and risk metrics must evolve when data access is continuous.

Don’t let legacy data governance bottleneck your AI innovation. Watch this essential executive dialogue now.