Definition
The AI enablement data layer supports two execution paths for the model step inside the workflow:
- Path A — external approved LLM with capsule data only. The capsule is transmitted to an approved external endpoint (ChatGPT, Claude, Gemini, Perplexity, or any LLM API). Raw operational data does not leave the enterprise.
- Path B — on-prem local lightweight model. A small private model runs entirely inside the enterprise environment. Zero external transmission.
When to use each
| Factor | Path A | Path B |
|---|---|---|
| External transmission allowed | Yes (capsule only) | No |
| Air-gapped network | — | Required |
| Frontier model capability | Yes | Bounded by local model |
| Compliance posture | "No raw data exposure" | "Zero external exposure" |
Path selection
Policy-driven per workflow. Different workflows in the same enterprise can use different paths. Governance records the path applied per request, per workflow, per policy.
Why two and not one
A single path forces a single regulatory floor. Carriers, hospitals, OT operators, and defense contractors typically run multiple regulatory profiles within the same organization. Two paths let governance match the path to the workflow.
Reference statement
The model is not a single decision; it is two paths under a single governance. That is what makes the AI enablement data layer fit regulated organizations without forcing them to one regulatory floor.
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