AI Coding Policy + ROI Template for Teams Adopting Cursor

A workable policy answers five questions and stops there: which models are approved, who pays and how spend is capped, what data may go in a prompt, how AI-generated code is reviewed and who breaks a tie when a reviewer and the AI disagree.

Five is not a magic number, it is the number that fits on one page, and length is what decides whether anyone reads the thing. The part teams leave out is conflict resolution, probably because it looks like the least technical of the five. Then a reviewer blocks a PR because they do not trust 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. Cursor documents group-scoped model access under Organization Groups on Enterprise, where group settings combine permissively with team settings: restrictive defaults at the team level, widened for the cohorts that need more. Check what your own dashboard exposes before you write that line as a control, and phrase it as a default people follow when it is not one.

The consequence of that callout is easy to miss. On Teams the only hard spending control is team-wide, because per-member limits are Enterprise. Included usage is allocated per person and does not transfer, so nobody can eat a teammate's allowance. On-demand spend behaves differently: it draws against the same team-wide cap for everyone, which is why the spend paragraph earns its place even when the dashboard number looks generous.

These are the data rules security and finance will check for. Each one keeps sensitive material out of prompts and training data, and they are short enough to fit on the same page as the rest of the policy.

• 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. org-wide so code is never used for training.
• No secrets or PIIPersonally Identifiable Information. Data that can identify a person (names, emails, SSNs); regulated and sensitive. Press Enter for the full definition. in prompts: keys, tokens, customer data stay out of chats.
• Use `.cursorignoreA file listing paths Cursor must never index or read, kept separate from .gitignore. Press Enter for the full definition.` for sensitive paths (treat it as best-effort, not a hard guarantee).
• Approve commands that touch data, deploys or external systems.

Half that list is enforceable in the product and half is convention, and the policy reads better when it says which is which. 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. enforcement is a switch an admin locks org-wide, and command approval is a setting. The secrets rule and the ignore-file rule are asks: nothing checks them for you, which is the reason they need to be in writing next to the two that do get checked.

If you want the secrets rule enforced rather than requested, hooks are the deterministic layer. A beforeSubmitPrompt hook runs after someone hits send and before the backend request, and it can stop the submission, so a scanner wired there catches the string whether or not the author remembered the policy. It also gives a security reviewer something better than a promise about behaviour.

Keep this section phrased 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 org-wide" is checkable in an afternoon; "we are careful with customer data" gives a reviewer nothing to verify, and reviewers tend to read the second one as an absence of controls.

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

One exception: restate the review rule anyway.

Naming it in the AI policy settles the question of whether generated code is a different category of change before anyone has to have that argument in a PR thread.

Model version lists are the other thing to keep out of the body. The catalogue and its prices move when providers move theirs, and a policy carrying a stale model table teaches people that the document is out of date generally, not just in that row. Name the approval mechanism and link the live list.

I would also drop any blanket ban on agent-written code reaching production. It is the clause most likely to be quietly ignored, and teams that write it usually mean something narrower: generated migrations get a human-authored review, or nothing touches the payment path without a second pair of eyes. Write that narrower version and name the surface it applies to.

Measure your own numbers instead of borrowing a headline multiplier. Capture a baseline, run a pilot, then compare the same delivery metrics plus cost per developer a few weeks later. The four steps below give you a delta leadership can defend.

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

Step two says hold everything else constant, which is never quite true. Something always moves during a pilot: a release freeze, or two people out on leave. Name the confounders you know about in the report rather than hoping nobody remembers them, and if the pilot team happens to be your strongest team, say that too, since it is the first thing a skeptical reader asks.

One cost line surprises finance more than the seat price does. The Cursor Token Rate applies to input, output and cached tokens on non-Auto requests, and it applies when you bring your own API key too, so a model that reads "we already pay the providers, Cursor is just the seat" comes out low. Put the token rate in the cost row of the pilot report.