Public AI vs. Private Enterprise AI
“Enterprise AI” identifies a market. It does not identify a complete data boundary. “Private” may refer to restricted access, a dedicated environment, a contractual commitment or a combination of controls. Buyers need to determine which meaning applies to the proposed service.
The practical reader question is whether the intended information can be processed under conditions the organization understands and authorizes. A general-purpose service can offer contractual enterprise controls. An organization-controlled deployment can still transmit information through external integrations. Neither label resolves the assessment.
Follow the full data path
Map the flow from the user’s device through authentication, application processing, retrieval, model inference and response delivery. Add monitoring, support access, backup and deletion processes. Prompts and uploaded documents are only part of the information handled: retrieved excerpts, generated outputs and diagnostic logs can also contain sensitive material.
A useful architecture diagram identifies components, operators and crossing points. Where a component is outside the organization’s control, attach the applicable agreement and configuration evidence. Ask whether the diagram covers embeddings, indexing and observability services as well as the model endpoint. A statement about model hosting should not silently stand in for the entire application.
Compare verified conditions
| Dimension | Question for any candidate | Evidence to request |
|---|---|---|
| Processing | Which entities and locations process each data type? | Deployment-specific data-flow diagram and subprocessors |
| Retention | What persists in application, model service, logs and backups? | Retention settings, exceptions and deletion process |
| Training use | Can inputs or outputs be reused, under which terms? | Applicable agreement and account configuration |
| Access | Which users, administrators and support personnel can access content? | Role definitions and access test results |
| Isolation | How are organizations and departments separated? | Boundary design and representative negative tests |
| Exit | What can be exported, revoked and deleted? | Exit procedure and evidence of completion |
No universal “public AI” answer belongs in the right-hand column. Populate it for the service, account and deployment being purchased. A provider’s documentation for a different subscription is not evidence about this one.
An illustrative audit workflow
An auditor wants to use AI to summarize a confidential audit finding. Before uploading it, the auditor must check whether the organization permits that information to be shared with the service. A personal account and a company-managed account may use the same AI model but have different rules for storing data, accessing documents and using information for training. The company-managed account may offer stronger protections, but the audit team must verify its settings and contract before deciding whether it is suitable for the confidential finding.
The team records where the source document, retrieved extracts and response will go. It verifies whether logging retains content, which administrators can see it, and what happens when the auditor leaves. It then tests an unauthorized user attempting to retrieve the finding. A successful summary establishes usefulness; a denied cross-role request supplies different evidence about the boundary. Both matter.
If retention remains unresolved, management can defer sensitive use or approve a narrower trial with fictional documents. The auditor does not accept the risk by clicking an upload button. The designated business owner and authorized risk decision-maker determine the permitted scope after technical, privacy and legal review.
Keep the comparison operational
Before selecting a configuration, record the permitted purpose, data classes, users, owner and human-review requirements. State what changes trigger reassessment: a new integration, an expanded user group, a model service change or revised provider terms. A bounded approval should not become an informal authorization for every future workflow.
The NIST AI Risk Management Framework provides a voluntary structure for evaluating AI risk. This article’s comparison table is a practical editorial decision aid, rather than a prescribed NIST checklist.
Choose the service whose verified boundaries meet the workflow’s needs. Keep final authorization and professional judgment with people who are accountable for the information and the decision.
References
- SONO current platform terminology: naming and public positioning only.
- NIST AI RMF: voluntary risk-management framing.
Reference URLs opened successfully on 13 October 2026. External frameworks inform evaluation; they do not certify SONO or establish a legal obligation for every reader.
