How to Use AI to Review Code Without Leaking Secrets

Before pasting code into an AI tool, scan for API keys, tokens, passwords, private URLs, customer names, database dumps, and internal credentials. Even a short config file can reveal more than you intended.

A safer pattern is to replace sensitive values with placeholders such as API_KEY, CUSTOMER_ID, or INTERNAL_URL. The model usually does not need the real value to reason about the code.

Look beyond obvious secrets. Sensitive code can include:

- access tokens and refresh tokens - database connection strings - private package registry URLs - customer identifiers - internal hostnames - unreleased feature names - logs with user data - screenshots or stack traces containing paths and account names

If you are unsure whether something is sensitive, remove it or replace it with a neutral placeholder. The review may still work.

Do not paste a whole repository when a function, error message, and short explanation are enough. Smaller input reduces privacy risk and often produces a clearer answer.

If the AI needs more context, add it step by step. This also helps you notice when the model is guessing beyond the code you provided.

A good code review prompt usually includes:

- the language or framework - the goal of the code - the relevant function or component - the error message or concern - the kind of review you want - any constraints, such as "do not change public API"

For example:

```text Review this Next.js route helper for correctness and edge cases. Do not assume files that are not shown. Focus on bugs, missing validation, and tests. ```

This is safer and more useful than "review my project."

Sometimes the sensitive part is not a secret value but the business rule itself. A pricing rule, fraud check, customer segmentation rule, or unreleased product flow may be private even if it contains no credentials.

When possible, rewrite the example into a generic shape. Replace company-specific terms with neutral concepts. Keep the logic structure that needs review, but remove details that reveal strategy or customer information.

For example, "premium enterprise customer with internal discount rule" can become "eligible account with special discount rule." The AI can still reason about edge cases without seeing private language.

A useful review prompt asks the AI to separate issues by severity. For example: identify correctness bugs, security concerns, edge cases, and missing tests. This makes the response easier to evaluate.

You can also ask the model to explain why each issue matters. A good code review finding should connect a line of code to a concrete failure mode.

Ask for structured output:

```text Return findings grouped by: 1. Correctness bugs 2. Security or privacy risks 3. Edge cases 4. Missing tests 5. Readability improvements

For each finding, explain the failure mode and suggest a minimal fix. ```

This reduces vague feedback. It also helps you separate real issues from optional style advice.

AI suggestions can be plausible and wrong. Run tests, read the diff, and check whether the proposed change fits the project conventions.

Treat the model as a reviewer, not an authority. The final responsibility still belongs to the developer and the team.

Verification depends on the change. For a pure function, add or run unit tests. For a route change, run the app and check expected pages. For a build-time helper, run the production build. For security-sensitive code, ask a qualified teammate or follow the team's review process.

Do not paste AI-generated code directly into a project without understanding it. Read each line. Check whether it changes behavior outside the intended scope.

If AI found something useful, save the finding in your normal development workflow: a commit message, pull request note, issue comment, or review checklist. Do not leave important reasoning only inside an AI chat history.

This matters because code review is a team activity. The team needs to understand what changed and why, not just that an AI suggested it.

If the code contains regulated data, customer data, proprietary algorithms, credentials, or sensitive incident details, do not paste it into a general AI tool unless your organization has approved that workflow.

In those cases, use approved internal tools, local models, redacted reproductions, or human review. Convenience is not worth accidental exposure.