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A database schema can expose organizational context even when you never send a production row. Table and column names, relationships, and constraints may reveal what a system tracks or how a business operates. If you send that structure to a hosted large language model (LLM), the prompt is handled under that service’s terms, privacy policy, settings, and architecture. Ollama says its locally processed content is not transmitted to it; that protection applies to Ollama’s local processing, not automatically to every component on your computer.
Why a schema can reveal sensitive information
A schema describes more than storage mechanics. Names such as acquisition_target, fraud_review_status, or oncology_trial_arm can point to business activity, risk workflows, or research areas. These are illustrative examples, not documented incidents. Relationships between tables and constraints can add context about how those processes work.
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That makes sharing a schema a data-flow and governance decision, even if production records remain on your machine. The concern is disclosure of organizational context, not proof that a particular provider retains, trains on, or misuses every prompt. Provider practices differ, and the evidence described here does not compare other providers or establish a legal conclusion.
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What Ollama says about local and cloud processing
Ollama’s privacy policy, last updated March 2026, distinguishes local processing from its cloud-hosted models. These are the company’s stated practices, not independent audit findings.
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| Ollama mode | What Ollama says | Practical implication |
|---|---|---|
| Local processing | Ollama says it does not collect, store, transmit, or have access to prompts, responses, model interactions, or other content processed locally. | When inference is genuinely local, Ollama says it does not receive that content. |
| Cloud-hosted models | Ollama says prompts and responses are processed transiently to provide the service, are not stored beyond the time needed to fulfill the request, and are not used to train AI models. | The prompt is sent for cloud processing under Ollama’s stated policy. |
Read the full Ollama privacy policy for its wording and scope. A provider’s policy describes its commitments; it is not the same as an independent technical verification.
How to disable Ollama cloud features
Ollama’s FAQ documents two ways to turn off its cloud features. Choose one, then restart Ollama:
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- Set
disable_ollama_cloudtotruein~/.ollama/server.json. - Alternatively, set the environment variable
OLLAMA_NO_CLOUD=1.
With cloud features disabled, Ollama says cloud models and web search are unavailable. Check the Ollama FAQ for the documented control and current details.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsSpecial offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.
A cautious local workflow for synthetic data
Local generation can reduce transmission to Ollama when the model inference is performed locally and cloud features are disabled. Treat synthetic generation as a workflow to validate, not a guarantee that the result is anonymous, representative, or suitable for every use.
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- Minimize the prompt. Include only the tables, columns, types, relationships, and constraints needed for the development task. Leave out production rows, credentials, secrets, and unnecessary business details.
- Ask for synthetic examples. State the desired format and quantity, and make clear that the output should be invented rather than derived from supplied records.
- Validate with code. Check that generated values match declared types and constraints, and that foreign keys and other relationships are consistent.
- Inspect the output. Look for accidental reproduction of any seed examples or other content that should not appear. Do not treat the label “synthetic” as proof that data cannot reveal sensitive information.
- Use it only for an appropriate purpose. Decide whether the generated data is fit for the intended development or testing task; generation alone does not establish statistical representativeness or suitability for every use.
What local inference does—and does not—protect
Ollama’s local-processing statement covers content processed locally by Ollama. It does not establish that every application, extension, operating-system service, network tool, or model-management step in your environment is offline or secure. Review the components in your actual data flow and confirm that the request is being handled locally rather than routed to a hosted model.
Local inference also does not answer whether a candidate model can produce useful data for your schema. No universal model choice or quality guarantee is established here. Evaluate candidates against your target schema for structural validity, constraint adherence, relationship consistency, plausibility, and the computing resources available; base a choice on results from that evaluation.
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