Remove Sensitive Data Before Using Cursor

Scrub PII Before Using Cursor AI Editor

Cursor sends code context to frontier AI models by design. The editor bundles your current file, recent edits, terminal output, and optionally entire codebase indexes into prompts it sends to models including Claude and GPT.¹ Developers using Cursor on production systems must assume that anything in their editor can reach these providers.

Unlike traditional editors, Cursor's AI features actively read your project to improve suggestion quality. Consequently, pasting a secrets-laden config file, a .env example, or log output containing internal IPs into a Cursor project creates a direct path for those credentials to reach AI providers. Pre-scrubbing before pasting into Cursor removes this risk at the source.

How Cursor shares context with AI models

Cursor's Chat and Composer features send your current file, selected code, and optionally additional files to the configured AI provider. The Codebase Indexing feature ingests your entire repo into an embedding store, which Cursor queries to build relevant context for each AI request.² Building on this: if your repo contains hardcoded credentials in any file, even a commented-out example or a test fixture, those values can be included in the context sent upstream. The scrubber eliminates the risk by removing credentials before they enter the project.

Codebase indexing is the feature that makes Cursor powerful and risky simultaneously. When you index a repository, every file becomes searchable context for AI queries. A credentials file committed months ago, even if removed in a later commit, may still exist in the index until you rebuild it. Before indexing a repository that has ever contained credentials, scrub the sensitive files first or add them to .cursorignore. The scrubber handles the text-level removal; .cursorignore handles the index-level exclusion. Together they close both paths.

What the scrubber detects in developer files

For developer content, the scrubber detects all major credential formats: AWS access keys, GitHub and GitLab PATs, Stripe payment keys, SendGrid and Twilio keys, NPM auth tokens, generic API keys, JWT tokens, PEM private key blocks, database connection strings, and internal hostnames. Furthermore, it catches IPv4 and IPv6 addresses and email addresses, which are common in stack traces, CI logs, and error messages that developers paste into Cursor for debugging help. Each unique value gets its own numbered token so the restoration step is exact.

Cursor users frequently paste terminal output, CI logs, and error traces into Chat for debugging assistance. These text sources are rich in sensitive data: a single failed deployment log can contain database connection strings, internal IP addresses, and authentication tokens all in one paste. The scrubber processes this unstructured log output the same way it processes code: paste the log text, and every detected credential pattern gets tokenized. The AI receives the error structure and diagnostic context it needs for debugging without receiving any of your production secrets.

Privacy mode and its limits

Cursor offers a Privacy Mode setting that promises not to store code on Cursor's servers or use it for training.¹ Yet even with Privacy Mode enabled, your code is still transmitted to the underlying AI provider (Claude, GPT-4, etc.) during the request. Privacy Mode controls what Cursor does with the data, not what the AI provider does. Consequently, organization-level AI usage policies that restrict sending production credentials to any external service apply even in Privacy Mode. Scrubbing before pasting satisfies those policies regardless of Privacy Mode status.

Think of Privacy Mode as a control over Cursor's behavior, not the AI provider's behavior. Your code reaches the AI model provider regardless of Cursor's privacy settings, because the model provider needs the code context to generate a response. If your organization's policy restricts sending production credentials to any external service, the policy applies to the AI model provider as much as it applies to Cursor. Scrubbing before pasting is the only control that enforces the policy at the data level, independent of which Cursor settings are configured.

Cursor's codebase indexing: embeddings, models, and data destinations

Cursor's Codebase Indexing feature generates vector embeddings of your source files using an embedding model served through Cursor's API infrastructure.² Cursor sends file contents to its embedding service, which returns vector representations stored locally in .cursor/index/. The indexed embeddings let Cursor perform similarity search across your codebase when assembling context for Chat and Composer requests. Files indexed at any point remain retrievable for context assembly until you delete or rebuild the index.

You control which files enter the index using a .cursorignore file at the project root, which follows the same glob syntax as .gitignore. Adding *.env, secrets.yaml, and *.pem to .cursorignore prevents those files from being embedded and included in context retrieval. However, .cursorignore only prevents automatic indexing. A file you explicitly reference with @File in a Cursor Chat message is sent to the active AI model regardless of .cursorignore, because explicit user references bypass the ignore rules by design.

Keeping credential files out of the Cursor index permanently

Place credential files outside the project root entirely, or use a secrets management tool such as HashiCorp Vault, AWS Secrets Manager, or the 1Password Secrets Automation SDK to inject credentials at runtime rather than storing them in files within the project directory. When credential files must exist in the project directory, add their exact paths to .cursorignore before running Cursor for the first time on that project. Rebuilding the index after adding new exclusions removes previously indexed embeddings for the excluded paths from the local store.

AI model selection in Cursor and its effect on data routing

Cursor routes inference requests to the selected AI model. Each model option sends your context to a different provider: Claude models route to Anthropic's API, GPT-4 and o3 models route to OpenAI's API, and Gemini models route to Google's API.³ Switching models in the top-right model picker changes the data destination for every subsequent request without a separate notification about the privacy policy change. A developer who uses Claude for most sessions and switches to GPT-4 for a specific task sends that session's context to OpenAI rather than Anthropic.

Your organization's AI governance policy may approve different AI providers for different data categories. Code analysis using open-source algorithms may be permissible with a broader set of providers than analysis involving proprietary business logic or internal service credentials. Scrubbing credentials and internal hostnames before pasting into Cursor Chat eliminates the provider-specific routing concern: the context that reaches any provider contains no identifiable sensitive values regardless of which model is selected.

Cursor Business plan and organizational data controls

Cursor Business accounts provide a centralized admin panel where admins can enforce model availability, disable personal API key use, and enable privacy mode across the organization. Business plan privacy mode promises that code is not retained by Cursor for training and is not sent to any third party beyond the AI model provider. Verify these settings at cursor.sh/settings before assuming business plan protections are active for your organization's seats, as they require explicit enablement rather than being on by default.

Terminal integration and diff view: additional data inputs to Cursor AI

Cursor integrates an AI-accessible terminal that lets you run shell commands and reference the output in Chat messages using the @Terminal reference. When you run a command in the Cursor terminal and then type @Terminal in Chat, the terminal output is attached to the prompt and sent to the active AI model. Terminal output from deployment scripts, database migrations, and CI runner commands frequently contains internal hostnames, connection strings, and authentication tokens embedded in error messages.

The diff view in Cursor, accessible through the Source Control panel, shows file changes alongside an AI Chat interface. Requesting AI review of a diff that includes credential changes (for example, rotating an API key in a config file) sends both the old and new credential values as diff context to the AI provider, so clean a credential-rotation diff before AI review to keep neither value from ever reaching the model.

Pasting CI/CD output into Cursor for debugging

CI/CD pipeline output accessed via GitHub Actions run logs, GitLab CI job output, or Buildkite build artifacts frequently contains environment variable values in error messages, command traces produced by set -x, and initialization scripts that echo configuration. Before pasting any CI output into a Cursor terminal or Chat message, run it through the scrubber. Pipelines using trace mode are particularly risky because every environment variable name and value appears in the debug output, making pre-paste scrubbing an essential step for any developer using Cursor for CI failure analysis.

Running every CI log through the scrubber before it reaches the Cursor terminal removes the highest-risk tokens at the source. Because CapyToolkit works entirely in your browser, the paste never leaves your machine during scrubbing, and the variables file you download lets you restore the real values locally once Cursor has returned its debugging analysis.

When to use this

Use this before pasting any file, log, config, or code snippet into a Cursor project that you want to keep isolated from external AI providers, especially production credentials, internal hostnames, or customer data.

Examples

ConnectionError connecting to redis://cache.prod.internal:6379 with password abc123xyz — from IP 10.20.30.40

ConnectionError connecting to [DBURL_1] with password [API_1] — from IP [IP_1]

STRIPE_LIVE_KEY=sk_live_51Ab
SENDGRID_KEY=SG.xyz123
INTERNAL_API=sk-internal-service-key

STRIPE_LIVE_KEY=[STRIPE_1]
SENDGRID_KEY=[SENDGRID_1]
INTERNAL_API=[API_1]