Is Cursor AI Training on Your Code? Privacy Settings Guide

8/11/2025

Is Cursor Training on Your Codebase? What the Default Settings Mean for Your Privacy

Hey everyone, let's talk about something that's been on a lot of our minds lately: AI coding assistants. It feels like they're everywhere now, & honestly, some of them are pretty amazing. Cursor, in particular, has been making waves. It's this sleek, AI-first code editor that promises to make you a hyper-productive coding machine. And for a lot of tasks, it really delivers.

But as soon as you start using it with your own projects, especially work stuff, a little voice in the back of your head starts asking the important questions. Where is my code going? Is it being used to train some massive AI model? Am I accidentally leaking company secrets?

These are not trivial questions. The answers are buried in privacy policies & technical documentation, which, let's be real, most of us don't have the time or patience to dissect. So, I did the digging for you. I’ve spent a ton of time going through the fine print, reading community discussions, & looking at how Cursor's tech actually works. Here's the inside scoop on what's really happening with your code.

The Big Question: Does Cursor Train on Your Code?

The short answer is: it depends entirely on your settings. This is SUPER important to understand because the default settings are different depending on your plan.

Here’s the breakdown:

If you're on a Free or Pro plan: By default, Cursor's "Privacy Mode" is OFF. When Privacy Mode is off, Cursor does collect your prompts, code snippets, & telemetry data. Their privacy policy is clear that they use this data to "evaluate & improve" their AI features. In simple terms, yes, your code can be used for training.
If you're on a Business plan (or part of a team): Privacy Mode is ON by default & is forcibly enabled. When Privacy Mode is on, Cursor guarantees a "zero data retention" policy. This means they don't store your code, & it's not used for training by them or any of their third-party AI providers.

This is the most critical takeaway. If you're a solo developer on a free or pro plan, you need to be proactive & manually enable Privacy Mode in the settings (

Settings > General > Privacy Mode

). If you work for a company, the business plan is designed to address these privacy concerns from the get-go.

A Look Under the Hood: How Cursor Handles Your Data

So what happens when you type a prompt or ask for a code suggestion? It's not as simple as your code just going to one place. It's a journey.

Your Editor to Cursor's Backend: First, your request (which includes the relevant code snippets from your open files) is sent to Cursor's own backend servers. These servers are primarily hosted on AWS. Even if you're using your own API key for an AI model, the request still goes through Cursor's infrastructure. This is where they do their "final prompt building" – essentially, they take the code you've provided, add some context, & format it in a way the AI model can understand.
Cursor's Backend to Third-Party AI Models: From there, Cursor sends the prompt to a third-party AI model provider. They use several, including OpenAI's models (like GPT-4), Anthropic's models (like Claude), & even some of Google's Gemini models.

This is where the privacy agreements become crucial. Cursor has a "zero data retention agreement" with OpenAI & Anthropic for users on the Business plan. This means that for those users, the AI providers don't store or train on their code.

However, for non-Business plan users (even with Privacy Mode on), there's a small catch: OpenAI & Anthropic may retain prompts for up to 30 days for "trust & safety" reasons. This is to monitor for misuse of their services. After 30 days, it's supposed to be deleted. So, while your code isn't being used for training, a temporary copy might exist on a third-party server for a short period.

Codebase Indexing: How Does It Work & Is It Secure?

One of Cursor's coolest features is its ability to understand your entire codebase. You can ask it questions about files you don't even have open, & it will give you intelligent answers. This is done through a process called "codebase indexing." But how does that work without storing all your code on their servers?

The answer is embeddings. Here's a simplified explanation:

Chunking & Uploading: Cursor breaks down your code into small, manageable chunks. These chunks are temporarily uploaded to their servers.
Generating Embeddings: Each chunk of code is fed into a machine learning model that converts it into a numerical representation – a long list of numbers called a "vector embedding." This vector captures the semantic meaning of the code. For example, the embedding for a function that sorts an array would be mathematically "close" to the embedding for a similar sorting function, even if the code itself is written differently.
Storing Embeddings: These embeddings (along with some metadata like obfuscated file names) are then stored in a specialized vector database. Cursor uses a service called Pinecone for this. Crucially, the original plaintext code is discarded immediately after the embedding is created.
Retrieval: When you ask a question about your codebase, Cursor converts your question into an embedding & then searches the Pinecone database for the code embeddings that are most similar. It then uses that retrieved context to generate a more accurate answer.

Is this secure? It's a mixed bag. On one hand, Cursor isn't storing your raw code, which is great. They also obfuscate file paths to add another layer of security. However, the field of AI security is still very new, & there are some theoretical risks associated with embeddings:

Embedding Inversion: It might be possible for a sophisticated attacker to reverse-engineer the original code from its embedding, although this is considered very difficult.
Data Poisoning: An attacker could potentially introduce malicious code into the embeddings, which could cause the AI to generate insecure or incorrect code later on.
Data Leakage: If the vector database isn't properly secured, it could be a target for attackers looking to steal the embeddings.

It's important to note that these are cutting-edge attack vectors, & there's no evidence that they're being actively exploited in the wild. But it's something to be aware of, especially for highly sensitive codebases.

How Cursor Stacks Up: A Privacy-Focused Competitive Analysis

Cursor isn't the only game in town. How do its privacy features compare to the other big players?

Feature	Cursor	GitHub Copilot	Amazon CodeWhisperer	Tabnine
Default Privacy Setting	Off (Free/Pro), On (Business)	On (Business), Off (Personal)	On	On
Data Retention for Training	Opt-in (by leaving Privacy Mode off)	Opt-out (for personal users)	No	No
On-Premises/Self-Hosted Option	No	No	No	Yes
Model Flexibility	High (OpenAI, Anthropic, Google)	Low (OpenAI models only)	Low (Amazon models only)	High (multiple models, can be self-hosted)
IDE Integration	It's a standalone IDE	Plugin for major IDEs	Plugin for major IDEs	Plugin for major IDEs

Here’s a quick analysis:

GitHub Copilot: It’s the most well-known competitor. For business users, it has strong privacy protections. For personal users, you have to manually opt out of data collection, which is a bit of a pain.
Amazon CodeWhisperer: Amazon has taken a very privacy-first approach. It doesn't train on user code, & it has a feature that can scan for & flag code suggestions that resemble open-source code with restrictive licenses.
Tabnine: Tabnine has made a name for itself by focusing on enterprise-grade security. It's the only one on this list that offers a fully self-hosted, on-premises option, which is a major advantage for companies in regulated industries.

Cursor's main advantage is its flexibility & power. It's a fantastic tool, but its privacy model requires more user awareness than some of its competitors, especially for individual users.

Best Practices for Using AI Coding Assistants Securely

So, what can you do to protect yourself? Here are some actionable tips.

For Individual Developers:

Enable Privacy Mode IMMEDIATELY: If you're using Cursor's free or pro plan, go to
1Settings > General > Privacy Mode
& turn it on. Make it the first thing you do.
Use
1.cursorignore
: Cursor has a feature similar to
1.gitignore
. You can create a
1.cursorignore
file in your project's root directory & list any files or folders you want to be COMPLETELY ignored by the AI. This is perfect for files containing secrets, API keys, or other sensitive information.
Be Mindful of Your Prompts: Don't paste large chunks of sensitive data or confidential information directly into your chat prompts. Even with Privacy Mode on, remember the 30-day trust & safety retention policy.
Review All AI-Generated Code: This should go without saying, but never blindly trust the code an AI gives you. It can be a fantastic starting point, but you are still the one responsible for the quality & security of your code.

For Businesses:

Invest in Enterprise/Business Plans: These plans are designed with privacy in mind. They usually come with centralized controls, enforced privacy settings, & better legal protections.
Establish Clear Usage Policies: Create guidelines for your developers on how they can & cannot use AI coding assistants. This should include what types of projects are appropriate, how to handle sensitive data, & what their responsibilities are for code review.
Evaluate Tools Holistically: Don't just look at the features. Scrutinize the privacy policies, data handling practices, & legal terms of any AI tool before you roll it out to your team.

When Your Data Can't Leave the Building: The Case for Custom AI

Here's the thing: for some companies, especially those in finance, healthcare, or government, sending any code to a third-party cloud service is a non-starter. The risks, no matter how small, are just too high. In these cases, even the best-in-class privacy features of tools like Cursor or GitHub Copilot aren't enough.

This is where custom, privacy-focused AI solutions come into play. When you need absolute control over your data, you need an AI that runs within your own environment. For tasks like internal support or customer-facing communication, this is becoming increasingly important.

For example, a lot of businesses are looking to use AI to improve their customer service. But they can't just feed their customer data & internal knowledge bases to a public AI model. That would be a massive privacy violation. This is where a tool like Arsturn becomes incredibly valuable. Arsturn is a no-code platform that helps businesses build their own custom AI chatbots. The key difference is that these chatbots are trained only on the company's own data & can be deployed in a way that keeps all sensitive information secure. It allows you to get the power of AI for things like 24/7 customer support & lead generation, without ever having to worry about your data being exposed or used for training other models.

It’s a different approach, for a different problem, but it highlights an important point: the future of AI in business isn't just about using powerful public models, it's about using the right models in the right way, with a strong emphasis on privacy & control.

So, what's the bottom line?

Cursor is an incredibly powerful tool, but it's not a magic box. Its privacy, especially for individual users, is something you have to actively manage. The "Privacy Mode" setting is your best friend. Turn it on, understand its limitations, & be smart about what you share.

For businesses, the equation is a bit more complex. You need to weigh the productivity gains against the potential risks & choose a plan & a tool that aligns with your security posture.

Hope this was helpful! AI coding assistants are here to stay, & being an informed user is the best way to get the most out of them without compromising your privacy. Let me know what you think in the comments.