This session delivers a pragmatic overview of core data-privacy principles tailored for organizations operating in dynamic marketplace environments. The discussion outlines the baseline regulatory constructs, the operational disciplines required to manage personal data responsibly, and the governance expectations that regulators and business partners now treat as non-negotiable. Attendees will gain a clear understanding of how data inventories, consent management, purpose limitation, vendor oversight, and security controls intersect to either mitigate or amplify enterprise risk. The session will translate these concepts into actionable practices that support scalability, reduce exposure to consumer-protection claims, and strengthen trust across platforms, partners, and users. Participants will walk away with a risk-aware, business-aligned privacy framework they can deploy immediately to shore up compliance foundations and enable sustainable growth.

The AI wave reshaped how we talk about marketplaces—but what actually matters for building durable platforms? This panel brings together investors and operators to cut through the noise and explore what's next: what separates platforms that last from those that don't, and where founders should place their bets today. We'll debate which trends are hype versus substance and what founders should be building to win tomorrow.

In this session, leaders from Uber and Incognia explore how cross-functional alignment can make or break a platform’s fraud strategy. Drawing from real-world experience in identity abuse, ban evasion, and high-impact safety incidents, the panel unpacks what happens when teams build “trust by design” and what goes wrong when they don’t. Key Takeaways: - Why platform abuse often thrives in the gaps between teams - How to align teams on shared metrics and fraud mitigation as a product function - The impact of downstream silos - How to empower cross-team defenses without added friction - Attendees will walk away with frameworks for breaking down silos, leveraging device intelligence and behavioral signals to surface fraud earlier, and building shared metrics that drive results across risk, product, and user trust.

This talk dives deep on practical architectures for deploying AI agents in fraud workflows, the human-in-the-loop patterns that actually work, and lessons learned building tools for risk teams at scale.

As AI reshapes how growth work is imagined, built, tested, and scaled across product and marketing, traditional pilot-to-ROI models are collapsing. The AI Lab–Factory operating model offers a practical approach for growth leaders to separate learning from delivery while creating a clear path to scale. It emphasizes that foundational “Base” investments, builder capabilities, and clear human–AI ownership must mature before real business value emerges. Rather than chasing premature automation, it provides concrete guidance on when to experiment, when to formalize, and how to turn AI learning into durable growth performance.