Cursor and Privacy

Cursor's Basic Operating Logic

Cursor leverages cloud-based big language models such as GPT-4 and Anthropic Claude for code analysis and recommendation. When Privacy Mode is not used, parts of the project may be temporarily processed on Cursor's servers for a short period of time in a non-identifiable way. “Enabled (all code remains private)” position:

• No line on the cursor side is permanently stored as plain text.
•  The memory is cleared after the operation is finished; data retention is reduced to “zero-retention”. 

Which model does Cursor use?

Cursor uses OpenAI models (e.g. GPT-4) by default. It sends data to OpenAI via API for analysis of the code. According to OpenAI's usage policies:

• The code is not used to train the model (unless specifically authorized).
• OpenAI applies the necessary security measures to protect privacy.

However, the data you send is processed in the cloud, so it is not local.

Is it suitable for use in corporate projects?

In sensitive or enterprise projects:

• If the company has a confidentiality agreement, it may not be appropriate to send code to the cloud.
• Since OpenAI APIs mostly work over an internet connection, the projects you use may be unintentionally exposed to external servers. For this reason, organizations with high data security sensitivity generally prefer AI solutions that run only on local machines or systems hosted in closed network (VPC) environments.

Is it possible to use local models?

That's right. With Cursor, Open Source models (e.g. LLaMA, Mistral, DeepSeek, etc.) can be run locally and used without sending data to OpenAI servers. In this case

• No code goes out.
• It is completely local.
• However, the response quality may not be as high as OpenAI.

Privacy Mode

Cursor offers its users a feature called Privacy Mode. When this mode is enabled: 

• Your code data is not stored by Cursor or third parties or used for training purposes.
• When turned on, Cursor AI does not store anything.

Code data is only temporarily processed for the duration of the transaction; once the transaction is complete, it is completely deleted and not permanently stored by Cursor AI.

Security Certifications

Cursor is SOC 2 certified. This means that Cursor meets industry standards for data security and privacy.

The data flow and level of privacy in Cursor may vary depending on the use case.

When using large language model providers like OpenAI or Anthropic, Cursor does not store your code directly. However, when an API request is made, the relevant code fragments are transmitted to the servers of these providers and processed. For security purposes, these providers may keep the submitted data in temporary logs for 30 days, especially for Free and Pro plans.

For Business Plan users, Cursor has different practices for data security and privacy than the standard plan. In this scenario, too, the data goes to the API provider, but for Business Plan users, providers such as OpenAI or Anthropic do not store the data permanently, so a “zero-retention” policy is applied.

Data privacy is maximized when using a Local LLM. Cursor allows the user to run open source AI models such as Llama, Mistral or DeepSeek on Docker or directly on their own device. In this scenario, since both Cursor itself and the LLM used run entirely on the user's device, no data is transferred to external servers, ensuring maximum privacy.

To summarize:

• Privacy Mode resets Cursor's data storage;
• The cloud can still use GPTs, but your code will still go to the API provider (30-day log except for the Business plan);
• For complete isolation, a local model needs to be established.

ETHICAL CONSIDERATIONS

Ethical considerations in the use of Cursor can be approached from different perspectives. Each perspective raises important responsibilities for both developers and corporate users.

From a privacy perspective, Cursor itself does not store user code. However, when the language model used is cloud providers such as OpenAI or Anthropic, submitted code may be temporarily logged on these platforms for 30 days. This can pose a serious privacy risk, especially for projects involving personal, financial or medical data. According to the European Union's GDPR and Turkey's KVKK regulations, this may be considered as “data transfer abroad” and may result in additional legal liability.

In terms of data security, the use of encryption protocols such as TLS during transmission and Cursor's certification of compliance with security standards such as SOC 2 are important elements of trust. However, since the data goes to an external provider, this provider may need to be held liable in the event of a data breach. Therefore, it is recommended that companies include explicit provisions on “data processing liability” in their contracts.

As part of customer and employer liability, most companies' internal policies prohibit the transfer of source code to external systems. Even OpenAI's keeping logs for 30 days may be considered as “data outsourcing” for some organizations. For this reason, the NDAs and security policies of the relevant organization should be reviewed; if necessary, local models (BYO-LLM) or enterprise API solutions should be used.

The principle of transparency underpins ethical software development processes. The party whose data is being processed needs to be informed about this processing. While this is usually not a problem in open source projects, it is important to inform the user in closed projects that contain customer data. In such systems, it is good for transparency to clearly state phrases such as “Artificial intelligence assisted development process is used”.

In terms of intellectual property, the license terms of the code used are very decisive. If the code developed or integrated is under the GPL (General Public License) or another special license, sending this code to third-party servers may be considered a license violation. Therefore, the license structure of all code should be carefully examined and in such cases, the option to work with local models should be preferred.

Finally, the energy and sustainability dimension should not be overlooked. Every cloud request means processing power and energy consumption in data centers. This indirectly increases the carbon footprint. Working with an open source language model running on a local GPU can reduce environmental impacts in the long run.

In conclusion, the fact that Privacy Mode does not store data on the Cursor side is a significant advantage; however, to ensure full ethical compliance, one should also consider the fact that cloud-based LLM providers can store data for up to 30 days. Therefore, if legal regulations and contracts allow it, cloud usage can be considered; otherwise, local models would be a safer approach.