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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches“Local inference” tells you where a model computes—not whether every part of an AI feature stays on your device. Downloads, catalog checks, request context, telemetry, optional logs and a separately configured cloud fallback can each follow a different path.
No specific app, version, configuration or network trace is established here, so there is no basis to claim that a particular product silently sent data elsewhere. The useful question is how to verify the route and payload for the exact setup you use.
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What does “local AI” actually guarantee?
Local inference means a model performs its computation on the device. It does not, by itself, prove that setup, updates, telemetry, logging or fallback are offline. Microsoft’s hybrid-design guidance treats local readiness and cloud fallback as separate decisions: check whether the local capability is ready, and call a cloud endpoint only when the user and organization permit data to leave the device. If cloud use is not allowed, the app should explain the requirement and disable or hide the feature. Microsoft’s guidance for choosing between cloud and local AI models also recommends recording which route was used and readiness or fallback errors without recording prompts or sensitive content unless approved.
That design guidance is not evidence that an unspecified app follows it. To make a product-specific claim, you need the application and version, its relevant settings, and evidence from the run being described.
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Which network paths can exist even when inference is on-device?
Model setup and catalog updates
Microsoft says Foundry Local runs inference on-device after the model has been downloaded and cached. Its initial model download requires internet access. The service may also attempt an optional model-catalog refresh; cached catalog information can support offline inference. Microsoft summarizes this distinction by saying, “The only network traffic is the initial model download and optional catalog metadata refreshes,” referring specifically to Foundry Local. That FAQ also describes Foundry Local’s supported execution paths: Qualcomm NPU, DirectX 12 GPU through WinML/DirectML, NVIDIA GPU through CUDA, or CPU fallback.
This is a product-specific description, not a guarantee about other tools or an app’s separate cloud fallback. A first-run download is network activity, but it is not the same thing as sending a user prompt for remote inference.
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Request context and cloud inference
Even when a user types no secret into a coding assistant, the request may contain surrounding source context. Google lists prompts and responses, conversation history, snippets from open files, snippets from adjacent files and cursor location as Gemini Code Assist Customer Data for Standard and Enterprise. JetBrains says AI Assistant may send requests and pieces of code to its LLM provider, together with context such as file types and frameworks. These are documented behaviors for those products; they do not establish that all assistants send the same context. See Google’s Gemini Code Assist security and privacy documentation and JetBrains’ AI Assistant data-handling documentation.
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For Gemini Code Assist Standard and Enterprise, Google says processing typically occurs at the data center closest to the request origin, but it does not guarantee regional processing. That statement should not be read as a claim about another vendor’s processing location.
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Telemetry is not the same as prompt logging
Google describes Gemini Code Assist telemetry examples that can include a request event without the request contents, response events, user reactions, accepted-suggestion character counts and UI interactions. It says engineers can access this telemetry to support product improvements. Telemetry of this kind is distinct from sending full prompt text, though the precise data categories matter. Google’s documentation lists these telemetry examples.
Google separately documents optional Cloud Logging for prompt and response logs, context, and metadata such as telemetry and accepted lines of code. When enabled, collected data is sent to Cloud Logging for organization administrators. Google says prompts and responses are not used to train its model whether or not logging is enabled. Logging destination and control therefore depend on the organization’s configuration, not merely on whether inference is local. Details are in Google’s guide to viewing Gemini Code Assist logs.
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Some local tools add a proxy or processing service
Codag documents a distinct design in which its local proxy routes model traffic directly to the user’s provider, while eligible large tool outputs plus minimum task context may go to Codag for transient processing. Codag says source code, diffs, configuration and unrecognized content pass through unchanged, and describes operational metrics as contentless. These are Codag’s own product claims, not independent verification and not a pattern that can be generalized to other local AI tools. Its account is at Codag’s privacy and data-flow documentation.
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Evaluate a particular setup as a series of states and data paths rather than relying on the word “local.” Record the app version, license or edition, operating system, settings, model status and organization policy; those details can change the answer.
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- Check model readiness. Determine whether the intended local model is installed, downloaded and usable before making a request. Separate first-run downloads and catalog refreshes from inference traffic.
- Find the fallback rule. Ask what happens if the model is absent, unsupported or not ready. Establish whether the app stops with an error or can use a cloud endpoint, what triggers that choice, and whether the user or administrator can block it.
- Inspect the request boundary. Identify whether the outbound request can include only typed text or also conversation history, open or adjacent file excerpts, cursor position, file types, frameworks, tool output or other context. Check the product’s version- and license-specific documentation.
- Separate telemetry from content logs. Find out which operational events are collected, whether they contain request content, whether prompt/response logging is enabled, where logs go and who can access them.
- Check geography and retention separately. Ask where processing occurs and how long each data category is retained. A statement about typical processing location is not a regional guarantee, and one vendor’s statement says nothing about another vendor.
- Observe a controlled run. If you are authorized to inspect your environment, test with non-sensitive material and correlate application logs, settings and network observations. A connection to a service alone does not prove that prompt content was sent; establish the destination and payload before making that claim.
- Set a fail-closed policy if required. If cloud processing is prohibited, disable it through supported controls and confirm what the app does when local inference is unavailable. Microsoft recommends explaining the limitation and disabling the feature rather than routing data to the cloud without permission.
For operational diagnostics, route, readiness and error events can help explain behavior without retaining prompts, tokens or sensitive content. Microsoft recommends avoiding such content in logs unless it has been explicitly approved.
What can—and cannot—be concluded without a trace
Official documentation can establish how a named product describes its own model downloads, context handling, telemetry, logging or fallback design. It cannot establish what an unidentified installation transmitted in a particular session. A defensible report of a real fallback needs the app and version, configuration and policy, the triggering conditions, and evidence tying the observed route to the content sent.
Keep the categories separate when describing results: local inference, setup traffic, cloud inference, request context, operational telemetry and administrator-configured content logs are different claims. A product may have one without another, and a documented capability is not proof that it was used in a specific run.
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Questions to ask before using local-first AI with sensitive work
- What exact condition triggers cloud fallback, and can the app be configured to stop instead?
- What context is sent with a request, including nearby code, conversation history and tool output?
- Does model installation or catalog refresh require a connection, and can the feature work offline after setup?
- Which telemetry events are collected, and do any contain prompt or response content?
- Are prompt, response or context logs enabled? Where are they stored, and who controls access?
- What geography and retention commitments apply to each recipient of the data?
- Can a user or administrator inspect the route and verify behavior for the exact app version and configuration?
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