Why Enterprise AI Security Has To Start Before Data Leaves The Source

Jeremy J. Samuelson, CTO/EVP, AI & Innovation, Integrated Quantum.

​Ask an engineering team to list every place a single customer record travels or is stored after an AI workflow runs. Then ask them how long it takes to come up with the complete answer.

In my experience, they can’t answer because they rarely finish. Somebody gets through the obvious ones (the feature set, the model input, the prediction). Next, the less obvious places where data ends up start to surface, such as the vector store, the serving trace, the logs and the notebook a data scientist ran last March and never cleaned up.

Everything listed becomes an asset that must now be secured, monitored, retained correctly and eventually deleted. Additionally, everything mentioned was likely created by a workflow that wasn’t considered moving data.

This is happening at scale. Boston University cited a 2025 McKinsey global survey that “found that 88% of organizations now use AI in at least one business function—up from 78% just one year earlier.” Check Point’s 2026 report clocked the average organization at 1,968 attacks per week in 2025. Neither number proves AI caused anything, but they show the reality companies are operating in. They’re adding data flows, automation and system access paths to an environment that was already hostile, and they’re doing it faster than they can inventory the result.

Most conversations about AI security start with the model. Can it be poisoned? Can a prompt manipulate it? Can someone walk off with the weights? Those are real questions, but they skip over a basic—yet important—question that people need to start with: What sensitive information is the model (and the sprawl of systems around it) allowed to receive in the first place?

Treating The Source As A Trust Boundary

Where This Argument Stops

I want to be precise about what source-side minimization doesn’t solve because many vendors in this space have a bad habit of implying it solves everything.