Privacy engineering for AI and enterprise systems
Build the data boundary into the system. We work with teams on privacy-focused engineering for AI workflows and enterprise software, starting with what the application actually needs to process.
Tell us about your projectMap the data before choosing the control
Describe where data enters, where it is transformed, which external services receive it and what is stored afterwards. Include attachments, operational logs and error reports. This gives the project a concrete boundary to implement and test.
Scope the integration around the workflow
A privacy project may need local processing, data minimization, masking and restoration, access controls or changes to an existing application. The right approach depends on the task, the environment and the information that must remain useful. We can discuss both a new system and a focused integration into an existing stack.
Agree on observable acceptance criteria
Use representative synthetic inputs to define what must remain local, what may leave and what the user should see when a control fails. Evaluate the complete request path and the usefulness of the result. Claims about protection should identify their limits; a detector or a deployment location alone does not establish that a whole workflow is private.
Experience behind the work
Before founding su.engineering, our team worked on 30+ enterprise projects across digital identity, privacy, AI, distributed systems and enterprise software. Velum, our own privacy product, is where we continuously apply and test these ideas.
What shall we build together?
Tell us the workflow, the sensitive data involved, the services and deployment environment, and the behavior you need to change.