What Happens to Your Code When You Use an AI IDE?

The first copy is usually local

Your repository begins as files in a local or remote development workspace. An AI IDE reads some of those files to answer a question or perform an edit. It may use the open tab, search results, symbols, diagnostics or earlier conversation. That does not necessarily mean the entire repository is uploaded. It also does not mean only the text you typed is transmitted. The important question is which excerpts become context for each model request.

Ask the assistant to show or cite the files it used. Check whether indexes are local, remote or mixed. A local index can help retrieve code without transmitting the whole tree, but the selected snippets may still leave the machine when a hosted model is called. The behavior depends on the product and mode.

Prompt construction and routing

Before a model sees a request, the editor or agent may combine your instruction with system rules, repository context, tool schemas and prior task history. That assembled prompt can be sent directly to a provider or through an application backend. Cursor's BYOK documentation says requests still pass through Cursor servers for final prompt building. Licuse's direct BYOK path sends selected context to the configured provider, while its hosted path runs through a backend and model router.

These routes can have different privacy, latency and billing consequences. A personal key does not by itself prove there is no intermediary. If a model is selected from a router, the downstream provider may vary. Read the current product data-flow documents for the exact feature and plan you use.

Tools can create additional data paths

A coding agent may call repository search, terminal commands, a browser, documentation retrieval or an MCP server. Tool output can then become context for a later model call. A command that prints environment variables or a log with customer data can expose information the user never intended to include. Search queries and URLs may go to external services. The model request is therefore only one part of the data map.

Review high-impact tool permissions and minimize what commands return. Do not grant broad access simply because the first tool call was harmless. For confidential code, inspect which external servers are connected and what they can read or write. A trustworthy workflow makes each important action observable.

What happens to the response

The provider returns text, tool requests or other output. The agent may apply edits to local files, run a test and send the result back in another request. The editor may retain chat history locally or store account-linked usage elsewhere. The provider may keep records under its own terms. These stores are independent: removing a local conversation does not automatically erase provider logs or account data.

After accepting a patch, inspect the normal Git diff and run project checks. An AI explanation is not proof that a command succeeded or a file remained unchanged. Keep the final code and rationale in ordinary project artifacts so teammates can review the work without access to your chat session.

Training and retention are distinct

A provider can decline to train on API content while retaining data briefly for abuse monitoring or service operation. Another model endpoint may have a different policy. An editor's privacy mode can govern its own processing without controlling a provider account you connect. Read terms for the exact provider, endpoint and account tier, and obtain contractual assurances when required. Do not interpret local-first as a universal retention guarantee.

Licuse's data-practices page separates workspace files, direct BYOK, hosted requests and account services. That level of separation is useful for any product evaluation. Ask who receives each type of data, where it is stored, how long it is kept and which deletion action applies.

A safe way to test a new IDE

Use a non-sensitive repository and one simple prompt. Inspect the context selected, the network route if you can, the model used and the resulting usage entry. Then test a tool call and see whether its output is sent to a model. Read official documentation to explain what you observed. If you cannot explain a recipient, pause before using confidential material.

Create a team decision record for approved modes: product version, model route, provider terms, excluded paths, permission defaults and review date. Revisit it when a product updates or a new tool is connected. This turns the vague question of what happens to your code into a concrete, maintainable data-flow policy.

Ask different questions for different modes

When an IDE offers both a personal API key and a hosted credit plan, make two data-flow diagrams. The personal-key route may involve your provider account directly, while the hosted route can include the IDE's backend, a router and a downstream model. Account authentication and telemetry may be shared even when inference paths differ. Test the exact mode you intend to use, and do not generalize a privacy promise from one route to the other. Record who can delete local files, account records and provider logs. This distinction is especially important when a team moves from a beta BYOK setup to a future paid hosted plan. Document the model ID and any optional search tools as well, because changing either can alter recipients even when the editor setting looks unchanged to the developer or reviewer.