How to Review AI-Generated Code Before You Merge

AI coding can turn a small ticket into a working pull request surprisingly fast.

The code compiles. The tests pass. The diff looks clean.

So you click Approve.

That is exactly where an AI-generated code review needs to slow down.

The problem with AI-generated code is not always obvious bugs. Sometimes the code looks completely reasonable while making a wrong assumption about permissions, customer data, security, or how the existing application works.

A developer can ask an AI coding tool to add a feature and get a polished implementation in minutes. But the tool does not automatically know which business rules matter most, which tenant owns a record, or what should happen when the wrong user calls the endpoint.

That is why code review for AI-generated code needs a slightly different order.

You do not need to distrust every line.

You need to verify the parts that can cause the most damage first.

This guide gives developers and tech leads a practical AI code review checklist for how to review AI-generated code before it reaches production. For related delivery patterns, see our guide on how to add AI to existing software and human-in-the-loop workflows in production.

Key Takeaways

• AI-generated code should be reviewed for security, permissions, data access, logic, and tests, not just formatting.
• Treat an AI coding tool like a fast junior developer: useful for producing a draft, but not responsible for the final decision.
• Start with the boundary of the code: who can call it, what data it can access, and what it can change.
• Test more than the happy path. A feature that works for one user or tenant can still fail for another.
• Do not rewrite every AI-generated file. Fix the risky parts and keep the useful work.
• A good pull request should explain what changed, why it changed, and what was tested.

Why AI-Generated Code Needs a Different Review

Traditional code review already looks for bugs, security problems, missing tests, and design issues.

So what changed?

The speed and volume of code.

AI coding tools can generate a large amount of code before a developer has spent much time thinking through the implementation.

That changes the review problem.

A human developer may spend time considering:

• How this fits the existing architecture
• What happens with another user
• What happens with missing data
• Which permissions apply
• Which existing service should be reused
• What could break somewhere else

An AI coding assistant can produce a convincing implementation from the information it has been given.

That implementation may be correct.

It may also be based on an assumption nobody intended.

Current developer research shows why this matters. Sonar's 2026 developer survey found that AI-generated code represents a significant share of committed code, while trust and verification have not increased at the same pace.

The lesson isn't:

"Don't use AI for coding."

It is:

"Don't confuse fast code generation with verified software."

The First Question Should Not Be "Does the Code Look Good?"

This is where many reviews start.

Is the method clean?

Are the variable names clear?

Does the implementation follow the team's style?

Those questions matter.

But they should not be first.

Start with:

• Who can use this?
• What data can it access?
• What can it change?
• What happens if the wrong user calls it?
• What happens if it runs for another customer or tenant?

These questions expose problems that a clean-looking diff can hide.

A method can have excellent naming and still expose data to the wrong user.

A query can return the expected records in development and return another customer's records under a different tenant.

An endpoint can work perfectly for an administrator while failing to enforce the correct permissions for a normal employee.

Security and boundaries come before code style.

1. Check Authentication and Permissions First

Before reviewing the implementation line by line, check who is allowed to reach it.

Ask:

• Is the endpoint protected?
• Is the correct role required?
• Are permissions checked at the right level?
• Can a user access an action simply by knowing the URL?
• Does the frontend permission match the backend permission?
• Can a lower-privileged user call the same API directly?

This is especially important with AI-generated code because the assistant may focus on making the requested feature work without fully understanding the application's authorization model.

For example:

A developer asks an AI tool:

"Add an endpoint that lets managers update employee records."

The generated code may correctly update the record.

But the important question is:

Can a normal employee call the same endpoint?

The feature works.

The boundary doesn't.

That should block the merge.

2. Check the Data Boundary

The next question is:

Whose data can this code access?

This becomes critical in multi-tenant applications.

Imagine:

• Tenant A has 500 customers.
• Tenant B has 700 customers.

The feature works perfectly when tested with Tenant A.

But one query is missing the tenant filter.

Now the implementation is technically functional.

It is also a serious production problem.

When reviewing AI-generated code, deliberately look for:

• Missing tenant filters
• Missing user filters
• Broad database queries
• Unrestricted IDs
• Cross-account access
• Missing ownership checks
• Data returned from another scope

For ASP.NET Zero, this can include checking how tenant context and authorization are handled around services and queries. The same boundary thinking applies when you integrate AI into SaaS products that already enforce tenant isolation.

The technology can change.

The review question does not:

Can this request access data it does not own?

3. Test the Second User, Not Just the First

The happy path is usually the easiest path to test.

You log in.

You click the feature.

It works.

Done.

Not quite.

For AI-generated code, deliberately test a different context.

Try:

User A → works

Then:

User B → should only see what User B is allowed to see

For multi-tenant software:

Tenant A → works

Then:

Tenant B → must stay inside Tenant B

Also test:

• Unauthorized user
• Missing record
• Invalid ID
• Empty data
• Duplicate request
• Expired session
• Unexpected input

This is where a lot of the real review value comes from.

You are not trying to prove that the feature works.

You are trying to discover how it fails.

4. Then Review the Happy Path

Once the boundaries look correct, review what the feature actually does.

Now ask:

Does it solve the ticket?

Not what the AI interpreted.

Not what the UI appears to do.

What did the requirement actually ask for?

Then check:

• Does the logic match the requirement?
• Are existing services being reused?
• Did the AI create a duplicate helper?
• Are error cases handled?
• Are failures visible?
• Are exceptions being swallowed?
• Are database operations safe?
• Is the code changing anything outside the scope of the task?

AI coding tools are very good at filling in patterns.

That also means they can sometimes produce extra code that nobody asked for.

A smaller pull request is easier to understand.

Review the change against the requirement, not against how impressive the implementation looks.

5. Look for Security Problems

AI-generated code should receive a deliberate security pass.

Check for:

• Hardcoded passwords
• API keys
• Connection strings
• Secrets in logs
• Unsafe input handling
• Missing authorization
• Unvalidated file uploads
• Unsafe SQL
• Insecure deserialization
• Excessive permissions
• Sensitive information returned by APIs

Do not assume a test passing means the code is safe.

A test may confirm:

"The user can upload a file."

It does not necessarily confirm:

"The user cannot upload a file that should not be accepted."

This is why AI code review should combine normal testing with security thinking.

6. Check What the AI Changed Outside the Ticket

One of the easiest things to miss in a large AI-generated pull request is unnecessary change.

You asked for:

"Add a customer export button."

The AI changed:

• Export service
• Customer repository
• Shared utility
• Authentication helper
• Database model
• Logging
• Frontend state management

Maybe all of it is necessary.

Maybe it isn't.

Ask:

Why did each changed file need to change?

If the answer isn't clear, investigate it.

This doesn't mean AI-generated code should always produce tiny diffs.

It means every additional change should have a reason.

7. Review the Tests Differently

A green test suite is useful.

It is not proof that the implementation is correct.

AI can generate tests that confirm the same assumptions made by the generated code.

That creates a dangerous loop:

AI writes implementation → AI writes test → both agree → test passes

The code can still be wrong.

So don't only ask:

"Are there tests?"

Ask:

"What important mistake would these tests catch?"

Good tests should challenge the implementation.

For example:

Instead of only testing:

Admin can update employee

also test:

Employee cannot update employee

Instead of:

Tenant A can retrieve its records

also test:

Tenant A cannot retrieve Tenant B's records

Instead of:

Valid customer ID returns customer

also test:

Unauthorized user cannot retrieve the same customer

The best test is often the one that tries to break the assumption.

An AI Code Review Checklist You Can Paste Into a PR

Use this before approving an AI-assisted pull request.

Security

• Authentication is enforced
• Authorization matches the required role or permission
• No secrets or credentials were added
• User input is validated
• Sensitive data is not exposed

Data

• Correct tenant or customer scope is enforced
• Ownership checks exist where required
• Reads cannot access another user's data
• Writes cannot modify another user's data
• Database queries are limited to the required scope

Logic

• The implementation matches the ticket
• Existing services or helpers were reused where appropriate
• Error paths are handled
• Unnecessary changes were removed
• No unrelated behavior changed

Testing

• Happy path tested
• Unauthorized user tested
• Second user tested
• Second tenant tested where relevant
• Failure case tested
• Important write actions tested

Pull Request

• PR explains what changed
• PR explains why it changed
• AI-assisted changes are clearly identified
• Reviewer understands the risk
• No unresolved security or data-boundary issue remains

If one of the important security or data questions is unanswered, the code isn't ready to merge.

What You Should Not Do

Don't reject code just because AI wrote it

That defeats the purpose of using AI coding tools.

If the implementation is correct, secure, tested, and understandable, the fact that an AI helped write it isn't enough reason to throw it away.

Don't approve it because the demo worked

A successful demo proves one path.

Production has many paths.

The second user.

The second tenant.

The missing record.

The wrong permission.

The unexpected input.

The failed API call.

Those are the paths that deserve your attention.

Don't ask AI to "make it production ready"

That instruction is too vague.

Production-ready means different things to different teams.

Instead, give the AI specific constraints:

• Check authorization.
• Check tenant isolation.
• Add tests for unauthorized access.
• Do not change existing business rules.
• Do not introduce new dependencies without explaining why.

Specific instructions produce much more useful results.

Don't rewrite the whole file

Finding one risky line doesn't mean the entire AI-generated implementation is bad.

Fix the problem.

Add the test.

Review the boundary.

Keep the useful parts.

The objective of code review is not to prove that the reviewer can write the code better.

It is to make sure the code is safe to ship.

Should AI-Generated Code Be Reviewed by Another AI?

AI code review tools are becoming a real part of development workflows.

Tools can help identify:

• Potential bugs
• Security issues
• Missing tests
• Code smells
• Risky changes
• Possible regressions

But an AI reviewer should not become an excuse to remove human judgment.

The stronger model is:

AI generates → Automated checks run → AI reviews → Human verifies important decisions → Merge

Not:

AI generates → AI approves → Production

Current AI code review research increasingly focuses on this verification gap. AI-assisted development is increasing code volume, while human review remains necessary for security, architecture, and business context.

A Better Way to Think About AI Coding

AI coding changes the developer's job in one important way.

The value is moving away from:

"Can I write this code?"

toward:

"Do I understand what this code is allowed to do?"

AI can help produce the implementation.

The developer still needs to understand:

• The business rule
• The system boundary
• The data
• The permissions
• The failure cases
• The consequences

That's why experienced developers remain important even as AI coding tools become more capable.

The hard part isn't always producing the code.

It's knowing whether the code should exist in that form.

Frequently Asked Questions

Is AI-generated code safe?

It can be, but it should not be treated as automatically safe.

AI-generated code can contain security issues, incorrect assumptions, missing authorization, or logic that doesn't match the existing application.

Review and testing are still required before production.

Is AI code review different from normal code review?

The fundamentals are the same.

You still review correctness, security, maintainability, and tests.

The difference is that AI can produce convincing code very quickly, so reviewers need to pay particular attention to assumptions, boundaries, and edge cases.

Should every line of AI-generated code be reviewed?

Important code should be understood and verified before it is merged.

That doesn't mean manually rewriting or questioning every line.

Focus first on security, data access, permissions, business logic, and high-impact changes.

What is the best way to review AI-generated code?

Start with the boundary.

Ask:

• Who can call it?
• What data can it access?
• What can it change?

Then review the logic, tests, security, and failure cases.

Should developers disclose AI-generated code?

Teams should decide how AI-assisted development is documented in their workflow.

At a minimum, reviewers should have enough context to understand how the code was produced and what verification was performed.

The goal is not to shame AI use.

The goal is to maintain accountability for what reaches production.

Conclusion

AI coding tools are changing how quickly software can be produced.

That doesn't make code review less important.

It makes good code review more important.

The strongest approach is simple:

Check the boundary.

Check the data.

Check the permissions.

Test the failure.

Then review the code.

Treat AI-generated code as a fast first draft, not a finished product.

Because the most dangerous AI-generated code isn't necessarily the code that looks bad.

It's the code that looks good enough to merge before anyone asks what it is actually allowed to do.

Before you hit Approve, ask one final question:

"What could this code do that the developer didn't intend?"

That question may be more valuable than another 20 minutes of arguing about variable names.