Have AI code reviewed: Why a human needs to look at it

An code review for AI-generated software is an assessment by experienced humans before the application goes live: Is the code secure, does it process data correctly, are the dependencies trustworthy, and can it be maintained? Automated tools find typical patterns, but logic errors, missing permission checks, and incorrect assumptions about the business process are usually only recognized by a person who understands what the application is supposed to do.

With AI, applications are now created in days instead of months – customer portals, internal tools, automations. That is a real benefit. The problem: if you did not write the code yourself, you do not know its weaknesses. The French authority ANSSI and the BSI therefore recommend having AI-generated code reviewed by humans. The typical risks are outlined in Developing software with AI: The 7 biggest security risks.

What a review checks

Area	Questions
Access and permissions	Who is allowed to do what? Does the server check that – or only the interface?
Inputs	Are forms, uploads, and parameters processed securely?
Dependencies	Are all packages actually available, maintained, and up to date?
Access credentials	Are passwords or API keys stored in the code?
Data protection	Which personal data is stored where, and for how long?
AI functions	Is the application protected against prompt injection, and do agents have only the necessary permissions?
Operations	Updates, backups, logs, error handling – who takes care of this after launch?

What automated tools can do – and what they cannot do

Static code analysis, dependency scanners, and secret scanners belong in every project. They reliably and quickly identify known patterns. But they do not know that employees in one department must not see salary data, or that an order should only be placed after approval. Such rules are not in the code, but in the business process — and that is exactly where the most costly errors arise.

When a review is worthwhile

Before an application publicly accessible is.
Before they Customer data, payments, or personal dataprocessed.
When AI agentsare allowed to perform actions, such as sending emails or changing data.
When no one on the team fully understands the code.
After major changes and before the transition from prototype to production use.

How a review works

Brief discussion: What should the application do, who uses it, what data does it process?
Automated review: Code analysis, dependencies, credentials.
Manual review: Rights, data flows, AI functions, critical processes.
Result: prioritized list with concrete corrections – what to do immediately, what to do later.
On request: support with implementation and a second review afterward.

Are you developing software with AI support — on your own, with your team, or with a service provider? We take a look. A human review of code, dependencies, access rights, and configuration before your application goes live or processes customer data. Get in touch with us.

Frequently asked questions

Aren’t automatic security tools enough for AI code?

They are important, but they mainly detect known patterns. Logic errors, missing authorization checks, and incorrect assumptions about the business process are usually only recognized by a person who understands the purpose of the application.

How long does a code review take?

That depends on scope and criticality. A small internal tool can often be reviewed in a short time, while an application with customer data and payments needs more time. We clarify the scope in the initial consultation.