Data Integrity

LLMs love data, so it's only natural that folks building agentic systems tend to take a liberal approach. Every engineer's first question seems to be: how can I feed this thing as much data as possible?

But for those of us building in Healthcare, this goes against every instinct we have. We isolate and protect data because if we don't, it could mean real harm to patients. Decades of Healthcare governance work has honed the rules and standards we live by. Playing fast and loose with the data is just not an option.

Of course, LLM security is not just a problem for Healthcare. Prompt injection attacks need to be considered in any serious enterprise architecture. In this new world, data has taken on a new form. LLMs execute data directly. Direct string manipulation in a codebase will come to be seen like direct memory manipulation: an obvious attack surface. Strings are now considered harmful. If your architecture allows untrusted data to interact with secured data, you're doing it wrong.

The fundamentals of data engineering haven't changed, but new architectural constraints are in play. Any secure LLM architecture must follow them.

Separate process from data. Event-source all operations, but never embed data in the events themselves, only references to immutable blobs. The event log provides a complete audit trail of what happened without coupling it to the sensitive data itself.

Make the data shape explicit and enforced through schemas. Implicit structure is an attack surface when strings are executable. Schemas let you reason about data flow and audit it, without hydrating the data itself. Further secure the data using the principles of zero-trust.

At Anterior, working in Healthcare, we had to design within these constraints from day one. But as agentic systems move into finance, legal, and enterprise infrastructure, everyone will arrive at the same place. With LLMs in the picture, data integrity is a fundamental architectural requirement, not something you can bolt on after the first breach.