Julia Bardmesser

Fuel your morning with a stimulating exercise designed to engage, enlighten, and evolve. We’re not just setting the tone but pioneering conversations that matter, creating a dynamic platform in which brainstorming meets expertise. In deep-dive conversations led by our distinguished advisory board, get ready to reimagine your data strategies by unveiling the transformative ✅’s and ❌’s pivotal for driving unparalleled success. Once you’ve exchanged best practices and benchmarked with your fellow data leaders, our distinguished advisors will share their group’s findings with the entire audience from the main stage.

What happens when AI is deployed on unstable data? In high-uncertainty regions, unstable data can distort decisions with geopolitical consequences. In financial services, it can misprice risk, undermine regulatory defensibility, and erode executive trust. Different environments, but the same type of failure.

In this focused fireside conversation, Jillian Foster and Julia Bardmesser examine two contrasting case studies — one operating at the edge of geopolitical risk, the other inside complex financial institutions integrating legacy, structured, and rapidly expanding use of unstructured data. Gain a practical lens for:

• Assessing AI readiness
• Evolving governance from oversight to observability; and
• Ensuring data rights, metadata, and stewardship support scalable business value

Autonomous AI agents are increasingly being granted system access akin to human employees, creating a profound new layer of security and enterprise risk that most organizations are still struggling to manage. This session provides insiders' views on setting robust security frameworks and compliance standards, addressing the complexity of governing AI adoption that cannot be centrally controlled due to embedded vendor tools. Our panelists will detail the unique challenges posed by agent access—including defining authorization thresholds, managing access lifecycles, and implementing continuous monitoring to prevent data exposure and bad decisions. Attendees will learn essential processes required to balance rapid AI innovation against severe regulatory and security liabilities, moving past conceptual fears to establish clear, auditable governance rails for agentic capabilities.

• Implement distinct authorization and access management practices for AI agents
• Establish strong AI governance capabilities that integrate tightly with model risk management (MRM)
• Develop a current, comprehensive AI policy that clearly references existing privacy, cyber, and regulatory obligations
• Define clear security and monitoring metrics for AI agents