AI Coding Policy + ROI Template for Teams Adopting Cursor

Record one decision for each of the five questions, and stop at five so the policy fits on one page.

Conflict resolution is the row most worth writing down, because the need appears only when a PR stalls. A reviewer blocks an agent-written migration, the author points at the passing tests, and nobody has been named to break the tie.

Model access is the line most likely to end up aspirational. On Enterprise it is a real control: admins restrict models under Team Settings → Models, and Organization Groups can widen access for a cohort, because team and group access combine as a union. Teams doesn't get model access restrictions. Its admins can impose Auto, either as the default for each new chat or by locking the model picker to Auto. Write any narrower model rule as a default people follow.

Write the spend rule against how your team is billed. The mechanics differ between teams on The X SubscriptionThe subscription Cursor is sold through: one plan for Grok, Cursor and X, with Free, Plus, Premium, Super and Ultra tiers, per-seat Teams, a weekly included pool for Cursor models shared with Grok, and third-party models billed at API list price from prepaid credit. Press Enter for the full definition. and teams still on legacy seats.

Included usage is per person and doesn't transfer, so nobody can use up a teammate's allowance. Paid usage beyond the seats works the other way. Everyone draws on the same budget under one team limit, and when that limit is reached, paid usage pauses for the whole team until next month or until an admin raises it. That shared limit is why the policy needs a spend rule even when the dashboard looks generous.

Name the team-wide limit in the policy. Leave out a per-person cap unless your dashboard offers one. The team pricing docs list per-member limits for teams on The X SubscriptionThe subscription Cursor is sold through: one plan for Grok, Cursor and X, with Free, Plus, Premium, Super and Ultra tiers, per-seat Teams, a weekly included pool for Cursor models shared with Grok, and third-party models billed at API list price from prepaid credit. Press Enter for the full definition., but the help center keeps member-level limits to Enterprise, and the policy should only promise a control you have checked.

Put four data rules in the policy. They cover model training, what goes into prompts, which files the agent can read, and commands that touch data.

• Enforce Privacy ModeCursor's setting that guarantees code data is not used for training by Cursor or its model providers, and that an admin can enforce org-wide; data-retention terms are a separate, contractual layer. Press Enter for the full definition. for the whole team, so code is never used for training.
• Keep secrets and PIIPersonally Identifiable Information. Data that can identify a person (names, emails, SSNs); regulated and sensitive. Press Enter for the full definition. out of prompts: keys, tokens and customer data stay out of chats.
• List sensitive paths in .cursorignore, and treat it as best-effort rather than a hard guarantee.
• Require approval for commands that touch data, deploys or external systems.

Say which rules the product enforces and which depend on people. An admin can lock Privacy ModeCursor's setting that guarantees code data is not used for training by Cursor or its model providers, and that an admin can enforce org-wide; data-retention terms are a separate, contractual layer. Press Enter for the full definition. for the whole team. Command approval is a run-mode setting in each developer's Cursor, and Enterprise admins can enforce auto-run controls for everyone. The secrets rule and the ignore-file rule depend on people, so they need to be in writing.

To enforce the secrets rule instead of requesting it, use a hook. A beforeSubmitPrompt hook runs after someone hits send and before the backend request, and it can stop the submission. A scanner wired there checks prompts whether or not the author remembered the policy.

Hooks fail open by default, so a scanner that crashes or times out lets the prompt through. Set failClosed: true on that hook so a failure blocks the prompt instead. Hooks distributed from Cursor's dashboard don't take that option, so ship this one as a hooks file, in the repo or through MDM.

Phrase this section as facts about your configuration. "Privacy ModeCursor's setting that guarantees code data is not used for training by Cursor or its model providers, and that an admin can enforce org-wide; data-retention terms are a separate, contractual layer. Press Enter for the full definition. is enforced team-wide" is checkable in an afternoon. "We are careful with customer data" gives a reviewer nothing to verify, and reviewers tend to read it as a sign that no controls exist.

Leave out anything your engineering standards already cover, and anything you have no way to check. Code review, testing bars and release approval already have owners. Restating them in an AI policy adds pages without adding a rule.

Restate the review rule anyway. Naming it in the AI policy settles whether generated code is a different category of change before anyone argues the point in a PR thread.

Keep model version lists out of the body. The catalog and its prices change when providers change theirs, and a stale model table tells readers the whole document is out of date. Name the approval process and link the live list.

We would also drop any blanket ban on agent-written code reaching production, because it is the clause most likely to be quietly ignored. Write the narrower rule you mean, such as a human-written review for generated migrations, or a second reviewer for anything on the payment path, and name the surface each rule covers.

Measure your own numbers instead of borrowing a headline multiplier. Capture a baseline, run a pilot, then compare the same delivery metrics and cost per developer a few weeks later. The four steps below produce a before-and-after number that leadership can check.

1. 1Capture a baseline before rollout: cycle time, PR throughput and a quick time-spent survey.
2. 2Roll out to a pilot team, and hold everything else constant.
3. 3After 4 to 6 weeks, compare the same metrics plus cost per developer.
4. 4Report the change and the cost, not a borrowed headline multiplier.

Step two asks you to hold everything else constant, and in practice something always moves: a release freeze, or two people out on leave. Name the confounders you know about in the report. If the pilot team is your strongest team, say so, because a skeptical reader will ask that first.

Put the Cursor Token Rate in the cost row. Teams and Enterprise pay it on the input, output and cached tokens of eligible third-party requests, including those made with your own API key. Balance or Intelligence routes to those models pay it too. Cost mode, Cursor's own models and the excluded third-party models don't. A cost model built on "we already pay the providers, so Cursor is just the seat" comes out low.