Cursor can be safe for proprietary code, but the safe answer depends on how you configure its privacy settings and how your organization defines “safe.” Cursor provides a “Data Use & Privacy Overview” that explains what happens under different privacy modes. In short: if you enable Privacy Mode, Cursor says zero data retention is enabled for its model providers and that your code won’t be used for training by Cursor or third parties; if you turn Privacy Mode off, Cursor says it may store and use codebase data, prompts, editor actions, and snippets to improve features and train its models, and some data may be shared with model providers when you explicitly select their models. So “safe for proprietary code” is not a single yes/no—it’s “safe if you set the right controls and your compliance requirements match the product’s data flows.” :contentReference[oaicite:6]{index=6}
There are also operational details that matter for proprietary repos. Cursor states that even when you use your own API key, requests still go through Cursor’s backend because that’s where final prompt construction happens. It also notes that if you choose to index your codebase, Cursor uploads code in small chunks to compute embeddings; it says plaintext code used for embeddings ceases to exist after the request, while embeddings and some metadata (like hashes and file names) may be stored. Cursor also describes temporary caching of file contents for latency, using encryption keys that exist only for the duration of a request, and it highlights SOC 2 certification on the page. For many teams, these details are acceptable with Privacy Mode enabled and with clear rules about which repos may be indexed; for others (especially with strict internal policies), you may need enterprise controls, additional review, or a narrower “allowed workspace” policy. :contentReference[oaicite:7]{index=7}
If you’re building systems that involve sensitive data, treat Cursor like any cloud-assisted developer tool: reduce exposure rather than relying on hope. Keep secrets out of source control, use .env files locally, and never paste production credentials into prompts. If you need AI assistance on highly sensitive code, consider isolating it into a minimal repro project or a sanitized module rather than pointing the tool at your entire monorepo. This mindset also applies to data products you build: if you’re indexing internal documents for semantic search, your security boundary should live in your backend (auth, filtering, audit) and in your vector database layer—e.g., storing embeddings plus access-control metadata in Milvus or Zilliz Cloud—not in the editor. Cursor can accelerate the engineering work, but your production safety posture should still be enforced by your own access controls and pipelines. :contentReference[oaicite:8]{index=8}