Vibe Coding in Business: Where It Helps and Where It Becomes Tech Debt

Vibe Coding in Business: Where It Helps and Where It Becomes Tech Debt

Vibe coding means describing in plain language what you want, letting an AI write the code, accepting the result with little reading, and adjusting by trial and error. In a company, it helps with prototypes, simple internal tools and throwaway pieces of work. It becomes technical debt when the code touches customer data, money, or processes that nobody else can explain. The practical rule: the bigger the damage a mistake can cause, and the more people depend on the code, the more review and testing it needs.

Key takeaways

• Vibe coding is great for learning, validating ideas and building small tools that are low risk and have few users.
• The problem is not that an AI wrote the code. The problem is that nobody in the company understands, tests or can maintain what was written.
• Personal data, payments, permissions and integrations with production systems need human review and testing before any real use.
• Signs such as code nobody can explain, no tests, and passwords sitting inside the file show the tool has outgrown the prototype stage.
• A safe path is to map the use, limit data and access, pilot with logs, and only then decide who becomes the owner of the code.

What vibe coding is and what it is not

Vibe coding replaces line-by-line reading with a conversation loop: you ask, run it, check whether it works, and ask again. When the result appears on screen, it feels like the work is done. For people who do not program, this opens a real door: you can build a calculator, a dashboard or a form without waiting in a development queue.

What it is not: software engineering under another name. Engineering includes deciding what the system must not do, handling failures, protecting data, recording what happened and leaving the code readable for the next person. An AI can help with all of that, but only if someone asks for it, checks it and takes responsibility. Code that works in a demo does not prove it works on a Tuesday morning, with messy data and three people using it at the same time.

It is also worth separating two similar practices. Using AI as an assistant to a developer who reads, questions and tests each piece is one thing. Accepting everything unread and putting it into use is another. Almost all of the business risk sits in the second.

Where vibe coding really helps

There are uses where the cost of a mistake is low and the speed gain is large. They share a few traits: few users, no sensitive data, and code that is easy to throw away.

• Prototypes to validate an idea: a screen that shows how a workflow would behave, so the team can discuss it before hiring any development.
• Simple internal tools: a spreadsheet converter, a report generator that works from a file, a page that calculates an internal estimate.
• Single-use scripts: renaming files, cleaning a list, merging two tables for a task that will be done once.
• Learning: understanding how an integration works by running a small example in a test environment.
• Requirements drafts: using the prototype as a visual specification for a professional developer to rebuild properly.

In the last case, the value of vibe coding is twofold: the business team discovers what it actually needs, and whoever builds the final version receives something concrete instead of a vague description. If you want to start with a command-line tool without being a programmer, also read Claude Code for non-programmers, which covers the limits of this kind of use.

Where it becomes technical debt

Technical debt is the future cost of a shortcut taken today. In vibe coding, the shortcut is not understanding what was generated. The cost shows up later, usually when the tool has already become part of the routine.

The first risk scenario is the temporary tool that becomes permanent. It was born for one task, it worked, and now three areas depend on it. Nobody knows where it is hosted, who has access, or what happens if its author leaves the company.

The second is code that handles personal data. A system that stores customers' names, phone numbers or ID documents falls under Brazil's data protection law (LGPD), which requires decisions about purpose, access, retention and record-keeping. Code generated without that conversation tends to write data to unexpected places, expose a page without a login, or send information to outside services without anyone noticing. If the AI assistant used for coding runs outside the country, pasting real data into the prompt can itself be an international transfer. The article on international data transfer under LGPD when using AI providers explains what to check.

The third is code that acts in the world: it sends messages, changes records, issues charges, deletes entries. A logic error stops being a wrong number on a screen and becomes a customer charged twice or a message sent to the wrong list.

The fourth is maintenance. Every adjustment requested from the AI can change parts that nobody reviewed, and without tests there is no way to know what broke. The speed of the start is paid back with interest by the third or fourth change.

Signs that generated code needs review and testing

You do not need to be a programmer to notice that a tool has gone too far. These signs work as a checklist for the process owner and for whoever leads the area.

• More than one person or more than one department uses the tool day to day.
• It reads, writes or sends data about customers, employees or suppliers.
• It connects to another system with an access key, such as a CRM, ERP, database or WhatsApp.
• It does something that is not easily undone: charging, sending, deleting, approving.
• Nobody can explain in simple words what each part does.
• Passwords, tokens or keys appear written inside the code or in chat messages.
• There are no tests at all, neither manual nor automated, and the only proof it works is that it worked last time.
• When something goes wrong, there is no record of what happened, and the cause is found only by trial and error.
• The tool receives outside text, such as emails or customer messages, and passes that text to an AI model that decides what to do.

The last item deserves special attention. When a system passes outside text to a model, that text can contain hidden instructions. The article on prompt injection protection for AI agents explains the risk, and the list of known risks for language model applications maintained by the OWASP project is a good starting point for whoever will do the review.

If two or three of these signs are present, the code is no longer a prototype. It needs review by someone experienced, tests, and a defined owner. For tests written by the AI itself, which also call for healthy skepticism, read AI-generated tests: can you trust them and how to validate.

Illustrative scenario: from spreadsheet to a system nobody understands

Illustrative scenario: a services company with a small sales team uses a spreadsheet to build quotes. Someone in the department, with no technical training, asks an AI for a page that calculates the price from a few fields. It works well, the team adopts it, and the result is a useful prototype.

Then come the requests: keep a history of quotes, send the result to the customer on WhatsApp, pull the customer record from the CRM, allow a discount without approval up to a certain value. Each request is handled in minutes, and the tool grows. At some point it stores customers' names and phone numbers, has a CRM access key written in the code, and sends messages automatically.

The mistake is not having started. It is not having stopped at the point where the tool began to touch personal data, send messages and apply discount rules. At that point, the healthy path would be to treat the page as a specification, ask a developer to rebuild it with access control, logging and tests, and define who decides on rule changes. The prototype stays valuable: it showed what the team needs.

How to use vibe coding safely: a step-by-step plan

The method we apply in AI projects fits here with little adaptation: map, limit, pilot and hand over. Below is a lean version for any company.

• Map: write in a few lines which task the tool solves, who will use it and what data it touches. If the answer includes personal data or money, record that from the start.
• Classify the risk: decide whether it is disposable (single use), simple internal (few users, no sensitive data) or critical (customers, billing, integrations). Only the first two fit vibe coding without professional review.
• Limit: use fictional or anonymized data in tests, give the tool only the access it needs, and never paste passwords or keys into conversations with the AI. The principle of least privilege, described in OWASP's authorization guidance, is a good criterion.
• Pilot: run the tool with a small group, in a real workflow, with a record of what it does and an agreed success criterion. Write down the errors that appear.
• Review: before expanding use, ask someone experienced to read the code, look for exposed secrets, validate the business rules and write tests for the cases that matter.
• Hand over: define an owner, where the code is stored, how a change is made and how the tool is switched off. Without this, it should not leave the prototype stage.
• Decide: at the end of the pilot, choose between expanding, adjusting or stopping. Stopping is also a good outcome when the cost of maintaining the tool exceeds its benefit.

Who is accountable for the code and what rules the company needs

Vibe coding often starts outside the technology team, which is positive but creates a zone with no one responsible. A simple rule helps: every piece of code that goes into use has a named owner, even if an AI wrote it. The owner answers for data, access and changes.

It is also worth having a short policy on what can be pasted into AI tools. Customer data, contracts and credentials stay out by default, with approved exceptions. The piece on a company policy for employee ChatGPT use shows how to organize what to allow and ban, and the same logic applies to coding assistants.

For the technology team, the challenge is different: welcoming creativity without becoming a bottleneck. A good agreement is to offer a safe space for prototypes, with fictional data and no access to production, and a clear path for promoting what worked. The NIST AI Risk Management Framework helps with thinking through responsibilities and monitoring, and a company does not need to be large to apply it.

Finally, do not confuse productivity with the volume of code generated. What matters is whether the business problem was solved and whether the solution keeps standing without depending on a single person.

Frequently asked questions

Is vibe coding safe for a small company? It can be, as long as the use stays within prototypes, single-use scripts and internal tools without sensitive data. Safety comes from the limits you set and the review before expanding use, not from the AI tool itself.

Do I need to know how to program to do vibe coding? Not to get started, and that is exactly the appeal. But to decide whether the result can be used with real data, someone with technical experience needs to review the code, or you risk putting into use something not even its author understands.

What should I do with a vibe-coded tool that is already in use at the company? Start by mapping what it does, who uses it, what data it touches and where the code is stored. Then classify the risk and ask for a technical review with tests. If it touches customer data or performs irreversible actions, treat it as a critical system and name an owner.

Does vibe coding replace a developer? No, but it changes where the developer spends time. Instead of writing everything from scratch, they review, test and give structure to what the business team prototyped, which is usually a better use of their experience.

Sources

• OWASP: Top 10 for LLM Applications
• OWASP: Authorization Cheat Sheet
• NIST: AI Risk Management Framework