When we introduced AI-assisted coding tools, the main concern was not code quality. It was data handling. A single prompt that includes production logs, live identifiers, or credentials can create compliance and security issues that a code review will not catch. The tooling is capable; the risk is usually what we put into the context window.
This article describes how our team uses Cursor in environments where privacy and security requirements are non-negotiable. It reflects engineering practice, not legal advice. Your security and compliance teams should define what is permitted for your organization and your vendors.
Treat the agent as part of the supply chain
Cursor sits inside the development environment. It can read the repository, suggest multi-file changes, and run commands in the terminal. That is materially different from asking a question in a browser. We therefore treat the agent like any other component that processes information: useful, but not implicitly trusted with sensitive data.
The model does not know our data classification policy unless we encode it in workflow and repository configuration. Speed of generation is not a substitute for scope control or review.
Context boundaries
We use a simple three-tier model when deciding what may appear in prompts or indexed context.
Permitted. Source code, automated tests, synthetic or anonymized fixtures, and internal design documentation that contains no live customer data. This is the default working set for @codebase and for Agent mode when implementation is required.
Restricted. Staging configuration, heavily redacted metrics, and partial excerpts of non-production payloads. We typically use read-only modes first (Ask or Plan) and avoid uploading entire files or dumps into the chat.
Prohibited. Production databases and exports, real end-user records, authentication secrets, private keys, unredacted application or infrastructure logs, and support artifacts that may contain personal data. These do not belong in chat, in prompts, or in material we expect the tool to retain or process.
Technical controls reinforce the policy. Ignore rules for local secrets, pre-commit secret scanning, and keeping sensitive artifacts out of version control reduce the chance that sensitive data enters the index accidentally.
Context at a glance.
flowchart LR
subgraph permitted["Permitted"]
A[Source & tests]
B[Synthetic fixtures]
C[Internal docs]
end
subgraph restricted["Restricted"]
D[Redacted metrics]
E[Staging snippets]
end
subgraph prohibited["Prohibited"]
F[Prod data & logs]
G[Secrets & keys]
H[Live identifiers]
end
permitted -->|"@codebase, Agent"| Work[Day-to-day coding]
restricted -->|"Ask / Plan first"| Work
prohibited -.->|"Never in chat"| X[Out of scope]Repository standards
Informal team agreements did not scale. We committed the following to the repository so that new projects and new engineers inherit the same baseline.
Cursor rules state expectations that also apply in code review: no hardcoded secrets; synthetic data in tests and examples; minimal logging of identifiers; and changes limited to the stated scope of the work item.
Skills encapsulate recurring procedures—opening a pull request, running a structured self-review before request for review—so that good practice does not depend on who is available to pair.
Synthetic fixtures are reviewed like production code. Generated sample data often looks plausible; plausibility is not the same as compliance.
Recommended workflow
We align tool modes with a conventional delivery sequence.
Understand. Begin with read-only exploration: locate authentication, authorization, persistence, and existing tests. Confirm naming and module boundaries before editing.
Plan. Document scope, explicit non-goals, acceptance criteria, and the data-handling constraint for the task (for example, synthetic inputs only). Planning reduces large, unrelated diffs that are difficult to audit.
Implement. Use Agent mode in small increments—one logical change with corresponding tests—rather than broad refactors bundled with a bug fix.
Verify. Run the test suite and static analysis locally. Inspect the diff for new external endpoints, dependencies, log statements, and configuration that widens data exposure.
Review. Human review remains authoritative for intent, authorization, and audit requirements. Continuous integration and security scanning remain mandatory; they do not replace judgment on what the change is allowed to do with data.
Workflow at a glance
flowchart TD
U[Understand<br/>read-only] --> P[Plan<br/>scope & criteria]
P --> I[Implement<br/>small Agent steps]
I --> V[Verify<br/>tests & diff review]
V --> R[Review<br/>human + CI]
R --> S[Ship]Prompting
Effective prompts are short and bounded. We state the target area of the codebase, constraints on logging and data, expected test coverage, and definition of done. We avoid attaching production artifacts to diagnose behavior; we reproduce issues with synthetic or redacted inputs in a controlled environment.
Common failures
In our experience, most incidents are procedural rather than model failures: one-time pastes of production material into chat; pull requests that are too large to review carefully; governance documents that are not versioned with the code; and over-reliance on passing builds without reading the diff. None of these are unique to AI tools, but the tools amplify the cost of skipping steps.
Getting started
If you are adopting Cursor under similar constraints, a practical first phase is: configure rules and ignore patterns; complete one small change using Ask, then Plan, then Agent with tests; and agree explicitly that production logs and live identifiers are out of scope for chat-based assistance. Align that norm with how you already handle secrets in git.
We continue to refine this approach as vendor capabilities and organizational policy evolve. Feedback from security reviewers on agent-assisted pull requests has been as valuable as any internal checklist.
This post describes engineering practices only. Consult your security, privacy, and compliance functions regarding acceptable use, data processing agreements, and retention.
If your team uses coding agents under similar rules, what control failed first in practice—indexing, chat paste, or review capacity?
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