Most “AI infrastructure” advice treats security and efficiency as separate concerns — a security review here, a cost-optimization pass there. This methodology treats them as one analysis, because in practice the same architectural decision (where data lives, how it’s fetched, what touches it in between) determines both your attack surface and your token bill.
A physical AI workflow is any pipeline where an AI system reads from, writes to, or acts on infrastructure that exists independent of the model — file servers, databases, SaaS platforms, IoT/sensor networks, on-prem storage, or hybrid cloud systems. This is distinct from a purely generative workflow (prompt in, text out) because it introduces:
- Persistent state the AI doesn’t own
- Authentication and authorization boundaries between the AI and the resource
- Data transformation steps (rendering, chunking, OCR, parsing) that can leak, corrupt, or bloat information
- A cost structure with two currencies: compute/API tokens, and human/organizational trust
Every workflow in scope for this methodology has at least one point where an AI system crosses a boundary into infrastructure it doesn’t control.
EUS can help you analyze your workflows and the identify specific data processing demands of your workloads. Do you need to keep workloads domestically? Is your AI infrastructure security suitable for the data being handled? What compliance data must be available?