Designing a Safer Path for Proprietary Data

Proprietary data needs a defined route

Life-science organisations hold information that may be scientifically sensitive, commercially confidential, personal, or subject to contractual and quality requirements. Before that information is used in an AI-enabled workflow, the organisation needs a clear design for how it is selected, accessed, processed, retained, and reviewed.

There is no single architecture that fits every use case. The appropriate controls depend on the data, the task, the provider, the deployment model, and the organisation's own risk and governance requirements.

Build from approved evidence

A controlled workflow can begin with source material that has been identified and approved for the purpose at hand. Access can be role-based, the permitted actions can be limited, and outputs can be presented with the evidence needed for an accountable reviewer to assess them.

This approach makes it easier to see what the system was allowed to use, what it produced, and where human judgement remains necessary.

What a practical design should answer

• Which source materials are in scope for this task?
• What access, retention, and monitoring controls are required?
• Where will the workflow run and who is responsible for it?
• Which outputs may be used directly, and which require review before use?
• How will exceptions, corrections, and changes be recorded?

The Lonrú view

The goal is not to create a black box around sensitive information. It is to create a useful and proportionate working environment that respects the evidence, the people responsible for it, and the decisions that follow.

If you are defining the data boundary for an AI workflow, talk with Lonrú.