Enterprise AI quickly hits architecture fights: all private or public LLM APIs. Private sounds controlled but ops and evaluation stay in-house; public is fast but data egress and prompt leakage become audit issues. Wrong choices usually mean boundaries weren’t drawn first.
Cut by compliance, then choose by cost and outcome. Reverse the order and security review vetoes a demo that already shipped.
Don’t Replace Boundaries with Slogans
Customer lists, process parameters, unpublished financials—default no egress. Ticket classification, QC hints, knowledge Q&A may use cloud after de-identification. Where weights live, where logs live, whether prompts carry identifiers—write separately; “we take security seriously” isn’t a boundary.
Business pastes real documents into chat for effect—that’s not technical failure; boundaries weren’t enforced in the call chain. Without intercept, private can’t stop screenshot leaks either.
One Executable Cut
- Three data tiers: no egress, de-identified egress OK, public OK—default strictest.
- Two model paths: on-prem for identified data; public cloud only de-identified text; separate logs.
- Log every call: who, why, identifiers present—sample audits, not wait for incident.
- Vendor contracts: no training on your data, deletion SLA, breach notice—tech follows contract.
The XYN digital intelligence system can hang AI on tickets and QC with field-level egress control. Choose private vs public after boundaries are cut—then cost and effect compare fairly.
