Running the model locally reduces one data-transfer risk. It does not automatically secure files, APIs, logs, plugins, users or model outputs.
Understand the complete data path
Draw every component that receives the prompt or document: desktop interface, model runtime, retrieval index, transcription, web search, plugins, logs, backups and monitoring. Mark what stays on the device and what contacts another service.
A workflow is only as private as its least understood connection. Disable optional cloud features when strict local operation is required. Ollama documents a local-only configuration, while LM Studio documents offline operation after model files are downloaded.
Keep the model API private
Ollama binds to localhost by default, and its local API does not require authentication. That is suitable for same-device use, but it means you should not expose the port directly to the public internet.
For team access, place the model behind an authenticated application or protected network. Restrict inbound traffic, encrypt connections, rate-limit requests and define who may use which model and documents.
Changing a bind address is not an access-control system.
Protect documents and retrieval
- Encrypt devices and backups.
- Enforce permissions before retrieval, not inside a prompt.
- Separate document collections by role and purpose.
- Do not index secrets that users should never retrieve.
- Cite source files so people can inspect important answers.
- Define retention for uploads, extracted text, indexes and conversations.
Treat documents and tools as untrusted
OWASP identifies prompt injection as a leading risk for LLM applications. Instructions hidden in a document or web page can try to change model behavior. A system prompt cannot fully solve this problem.
Keep retrieved data separate from trusted instructions, validate outputs, limit tool permissions and require approval before consequential actions. LM Studio also warns that MCP servers can access files, run code and use the network, so install only trusted connectors.
Minimum launch checklist
- The runtime and model came from verified sources.
- Cloud features and telemetry choices are documented.
- The model port is not publicly exposed.
- Every user has only the permissions they need.
- Logs and backups have a retention owner.
- Prompt-injection and data-leakage cases were tested.
- A human approves high-impact actions.
- A shutdown and rollback procedure exists.
From checklist to policy
Assign an owner for models, data and access. Record every approved model version and connected tool. Review permissions after staff changes and rerun tests after updates.
For institutions without internal security capacity, Deviceterra's Project Sovereign can help scope a private deployment before confidential data is introduced.
Find a model your computer can run.
The LocalLens advisor applies conservative memory rules and tells you when the evidence is insufficient.
Run the free compatibility check →This guide is educational and reviewed for practical accuracy. Model software, licenses and hardware support change; verify official sources before a production deployment.