What Cursor Indexes and Where It Goes

Understanding what Cursor indexes and where it goes is essential for developers who want to leverage AI without compromising intellectual property. As AI tools become more integrated into the software development lifecycle, the mechanics of how they process your local files determine both their utility and their risk profile. This post examines the technical architecture behind codebase indexing, the differences between local and cloud processing, and how you can maintain control over your source code.

The Mechanics of Codebase Indexing

When you open a project in an AI integrated development environment, the tool creates a mathematical representation of your code. This process, known as indexing, allows the AI to understand the context of your entire project rather than just the file you are currently editing.

The indexing process generally follows a specific pipeline. First, the tool scans your file tree to identify relevant source files. It typically ignores files listed in your .gitignore to avoid processing build artifacts or dependencies. Next, it breaks the code into smaller chunks. These chunks are then converted into vectors, which are high dimensional numerical arrays that capture the semantic meaning of the code.

These vectors are stored in a vector database. When you ask a question or request a code generation, the tool performs a similarity search. It finds the code chunks most relevant to your prompt and sends them to the Large Language Model as context. The effectiveness of the AI depends entirely on the quality and depth of this index.

Privacy Modes and Data Transmission

Most modern AI editors offer different tiers of privacy. Understanding these settings is critical for compliance with corporate security policies.

1. Public or Default Mode: In many configurations, metadata or even code snippets might be used to improve the underlying models. This often involves sending data to the provider servers where it may be stored for a period.
2. Privacy Mode: This setting generally ensures that your code is not used for training models. However, the code still leaves your machine to be processed by the inference engine, such as Claude or GPT-4.
3. Zero Data Retention: Some enterprise agreements guarantee that the provider will not store any data sent for inference. The data is processed in memory and then discarded.

It is important to distinguish between where the index is stored and where the inference happens. Even if an index is stored locally on your machine, the act of asking a question usually requires sending the relevant code chunks to a cloud provider.

Local First Alternatives with Deska

For developers who require absolute control over their environment, a local-first approach offers a different security model. Deska is designed as a free desktop app for Mac, Windows, and Linux that prioritizes keeping your work on your own machine.

In a local-first architecture, the primary workspace and its configurations do not rely on a central cloud server to function. Deska allows you to run multiple panels in an infinite canvas workspace, which can include terminals, a code editor, and a browser. Because the app runs locally, your code, files, and sessions stay on your machine.

When using AI features within Deska, you have flexibility in how data is handled:

• Bring Your Own Key (BYOK): You can use your own API keys for AI services. This ensures that you have a direct relationship with the model provider and can audit exactly what is being sent through your own usage logs.
• Side by Side Agents: You can run coding agents like Claude Code and OpenCode side by side as panels. This allows you to compare how different tools interact with your code within the same local environment.
• Mobile Connectivity: Deska provides a mobile app to monitor work, but it uses a secure relay for direct device pairing. This avoids exposing ports to the internet while maintaining a local-centered workflow.

Managing Your Indexing Footprint

Regardless of the tool you choose, you should actively manage what gets indexed. Most tools respect a configuration file that dictates which directories are excluded from the AI index.

You should consider excluding files that contain secrets, environment variables, or large amounts of non-essential data like logs and documentation assets. Keeping your index lean not only improves privacy but also increases the speed of the AI responses because the search space is smaller.

If you are working in a highly regulated industry, look for tools that support managed inference or local models. This allows you to benefit from coding agents without your source code ever traversing the public internet.

Comparing Architectures: Cloud vs. Local Workspace

The choice between these architectures often comes down to the level of transparency you require. Cloud integrated tools offer convenience and seamless syncing across devices. Local-first tools like Deska offer a workspace that you control entirely, with the ability to integrate various AI models as you see fit.

Frequently Asked Questions

Does Cursor store my code on its servers?

When privacy mode is enabled, the code is generally not used for training, but it is sent to the model provider for inference. You should check the current terms of service for specific details on data retention periods for their index.

How do I stop some files from being indexed?

Most AI coding tools use a specific configuration file, such as .cursorrules or a standard .gitignore, to exclude paths. By adding sensitive directories to these files, you can prevent the tool from creating vector embeddings of that code.

Can I use AI coding tools without an internet connection?

Most advanced AI features require a connection to reach the Large Language Models. However, tools that support local-first operations or local model integration allow you to maintain your workspace and basic editor functions entirely offline.

Experience a Local First Workspace

If you are looking for a development environment that respects your data sovereignty while providing powerful AI capabilities, consider trying a local-first canvas. You can manage your terminals, code, and agents in one place without sacrificing privacy.

Download Deska for Mac, Windows, or Linux to start building on an infinite canvas where your code stays where it belongs: on your machine.