This is a legitimate concern. I've seen cloud bills spike from unintended data egress, so I treat security and cost risks with similar rigor. The core fear is that proprietary code will be sent to Cursor's LLM (OpenAI/Anthropic) and exfiltrated. Your goal is to move the conversation from blanket fear to controlled, risk-accepted configuration.
First, clarify that Cursor is an editor, not inherently a data pipeline. The risk is configurable. You need to present the security team with the specific controls that mitigate exfiltration to an acceptable level.
**Key Technical Controls to Present:**
* **Cursor's `cursor.rules` File:** This is your primary technical control. It allows you to explicitly block files, directories, and code patterns from being sent to the LLM.
```javascript
// .cursor/rules/cursor.rules
- "**/secrets/**" // Blocks entire secrets directories
- "**/*.env*" // Blocks all .env files
- "**/config/credentials.json" // Blocks specific credential files
- "**/node_modules/**" // Reduces noise and token usage
```
Enforce this via a team-shared rules file in your repository.
* **Network-Level Controls:** Advocate for an egress firewall policy. Cursor's primary outbound connections are to `*.openai.com` and `*.anthropic.com`. A deny-all, with explicit allow-listing for these domains (or blocking them entirely), provides a network backstop. This is a standard FinOps practice for controlling SaaS costs and applies directly here.
* **Model Selection & API Use:** The highest-risk scenario is using Cursor's bundled, paid "Pro" model which may send data by default. Propose a lower-risk alternative:
* Use Cursor in "Composer" mode with a bring-your-own-key (BYOK) for OpenAI/Anthropic. This shifts data processing accountability to your existing agreement with those providers.
* Or, more securely, disable all AI features in Cursor's settings (`Settings > AI`) and use it purely as a local editor. This is a valid starting point for acceptance.
**Propose a Phased Rollout:**
1. **Pilot Group:** Start with a small, trusted team (e.g., platform engineering).
2. **Audit Logging:** Ensure all pilot users have local logging enabled (Cursor settings) to audit all LLM requests. Review logs for a week.
3. **Strict `.cursor.rules`:** Implement and enforce the block rules for secrets and proprietary paths from day one.
4. **Cost & Usage Review:** Treat this like a new AWS service. Monitor for anomalous usage patterns that could indicate misconfiguration.
Frame the argument around **managed risk versus productivity debt**. The alternative is often ungoverned, shadow usage of web-based ChatGPT, which has zero local control or audit trail. A properly configured Cursor with rules and logging is a more secure and ultimately cheaper option than unmonitored alternatives. Provide them with the specific configuration files and firewall rules they can review.
Right-size or die