The Trust Stack: Designing Scalable, Secure Data Architectures for AI at Enterprise Scale - Chisom Nwokwu

This talk was recorded at NDC Manchester in Manchester, England. 
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Many companies are investing heavily in AI, but there’s a growing problem beneath the surface, most enterprise data architectures weren’t built to handle AI securely at scale. Sensitive data is moving through increasingly complex pipelines, often touched by multiple teams and systems. In the push to build models quickly and deliver business value, security and governance often take a back seat. That leaves organizations vulnerable, not just to breaches and compliance failures, but to broken trust with their users and stakeholders.

The solution? We need to start building AI systems on a foundation of trust. That means rethinking data architectures so they’re secure, scalable, and AI-first from the ground up. I call this approach “The Trust Stack.” It’s a layered way of thinking about architecture, where every part of the pipeline, from ingestion to model serving, has security, access control, data quality, and observability baked in. It’s not just about preventing attacks, it’s about designing systems people (and regulators) can rely on.

In this talk, I’ll unpack what The Trust Stack looks like in practice. We’ll explore the key layers of a modern AI-first data architecture, and I’ll show you how to embed security and governance at each step, whether it’s encrypting data at rest, tracking lineage for accountability, or securing access to feature stores and models. I’ll share real-world examples of what goes wrong when these layers are missing, and give you practical, actionable ideas to take back to your team. Whether you’re modernising legacy infrastructure or building new AI systems, this session will help you design data platforms that are both powerful and trustworthy.