Eleanor Treharne-Jones

Financial institutions face a unique challenge when adopting AI: innovation must move forward without compromising regulatory compliance, governance, or data security. While many organisations are exploring AI-powered analytics and automation, the reality is that trust in the underlying data remains the biggest barrier to scaling these initiatives. In this session, Aaron Honour, Head of Data Engineering and Architecture at Shawbrook Bank and Eleanor Treharne-Jones CEO at Bigeye will share how financial institutions can build the data trust foundations required to safely adopt AI and advanced analytics. Operating in one of the most regulated industries, Shawbrook is navigating the challenge many banks face: maintaining strict regulatory compliance while modernising data platforms and exploring AI-driven capabilities. The session will highlight how data observability and automated sensitive data scanning help financial institutions identify PII, monitor data quality, and understand lineage across complex pipelines. By embedding trust signals, such as data quality monitoring, lineage visibility, and automated classification, directly into their data platforms, organisations can improve transparency, reduce risk, and enable responsible AI experimentation without compromising compliance. If you’re working in financial services and trying to balance AI innovation with governance and regulation, this session will provide a practical blueprint for building trusted data foundations…

At Gartner Australia, Bigeye will show how enterprises can build trust in AI agents as they move into business workflows. Bigeye Agent Trust Hub connects agent activity to data quality, governance, lineage, ownership, policy, usage, and cost signals, giving data, AI, risk, and business leaders the visibility, auditability, and controls needed to move from fragmented AI activity to operational AI trust safely, efficiently, and accountably.

As Snowflake customers move from AI pilots to agents that act on business data, a new operational challenge is emerging: teams need visibility into which agents are active, what data they touch, who owns them, what they cost, and whether each interaction is trusted, governed and appropriate. In this session, Bigeye CEO Eleanor Treharne-Jones introduces a new approach to enterprise AI trust inside the Bigeye Enterprise AI Trust Platform. Learn how to connect agent activity to trust signals like lineage, quality, classification, ownership, policy, usage and cost, with a path from visibility into runtime controls, auditability and accountability.

Hear real world examples on how Fortune 500 companies deploy AI in production. Learn how a data access control plane ensures trustworthy AI, what prevents enterprises from trusting AI use cases, and how to quantify and communicate key trust signals. Explore three architectural models to measure, inform, and control AI data use, and see how Bigeye's AI Trust Platform helps teams earn trust with stakeholders, leaders, and customers.

Hear real world examples on how Fortune 500 companies are deploying AI in production. How to ensure trustworthy data use by AI with a data-access control plane. Break down what holds enterprises back