27/05/2025
Building AI products with sensitive data, but compliance and stakeholder complexity are slowing you down?
In industries like healthcare, finance, and the public sector, launching AI initiatives isn't just about models or infrastructure.
It’s about:
🔸 Navigating complex privacy laws and regulatory requirements
🔸 Managing ethics approvals, access controls, and audit trails
🔸 Supporting different user types - all with different risks and expectations
🔸 Scaling up without compromising on trust, governance, or usability
We experienced these challenges firsthand in our collaboration with Temerty Centre for AI Research and Education in Medicine (T-CAIREM) at the University of Toronto, where we developed the Health Data Nexus - a secure, compliant, and scalable platform for AI research using real-world health data.
🚀 The result? As of 2025, the platform supports secure AI research on over 15,000 patient records, including clinical tabular data, medical imaging, voice recordings, and population health information - serving users in 12+ countries across research, education, and clinical innovation.
How did we balance privacy, compliance, and usability?
Here's an overview:
✅ Compliance-First Architecture: Designed from day one to meet strict regulations, with formal Privacy Impact and Threat Risk Assessments, multi-zone access controls, and standardized data governance workflows.
✅ Scalable Infrastructure for Sensitive Workloads: Built on Google Cloud with Kubernetes and Terraform, supporting isolated research environments, regional data residency, and autoscaling for high-performance AI workloads.
✅ Role-Based User Experience: Tailored for researchers, educators, and data stewards, delivering secure, intuitive access and governance aligned with each user’s responsibilities.
For more details on how we approached the architecture, governance, and user experience, check out our case study 👇
https://bit.ly/4drF8Mj
