Hello, in the last few years the concept of “vibe coding” has spread at an incredible pace. We are now seeing even people with no professional software background build working products in just a few hours using only a handful of prompts.
But is vibe coding really producing production-ready products? Or has it just shortened the development time?
Because production-grade software still requires discipline around security, architecture, operations, and compliance.
So let’s start…
What is Vibe Coding?
Vibe coding is when a developer (or someone who isn’t a developer at all) builds a product quickly by instructing AI in natural language. An idea becomes a prompt, and that prompt becomes a working application within minutes.
Vibe coding is shortening the path from idea to product more than ever before in history.
This approach is a major breakthrough for technology. Because turning an idea into an MVP no longer requires a large team, weeks of development effort, or deep infrastructure knowledge.
Building Fast Doesn’t Mean Building Safely
Speed is always valuable. But there is a critical truth most people overlook here:
Building software is not the same thing as building a sustainable, secure, and scalable system.
Today there are hundreds of applications in the market that have been built in hours using AI and pushed to production without going through any technical review. And a significant portion of these applications unknowingly carry serious technical, operational, and legal risks.
The real question is this:
Is this system secure? Is it manageable? Is it scalable? Is it compliant with regulations? Can it survive a crisis?
Questions That Must Be Answered Before Going Live
Before pushing an application to production, you definitely need to run it through a checklist. Here are the most critical headlines I’ve come across in architecture processes over the years:
Security Side
- Have SQL Injection, XSS and other basic security tests been performed?
- Are API keys and secrets managed securely? (e.g. Azure Key Vault, AWS Secrets Manager)
- Have .env files or access credentials leaked into git history?
- Are rate limiting and abuse protection mechanisms in place?
- An OpenAI key embedded in the frontend (one of the most dangerous patterns).
- Are Authentication and Authorization flows correctly designed?
- Are personal or sensitive data being logged?
Operational Side
- Are logging, monitoring, and alerting processes defined?
- Are backup and disaster recovery scenarios in place?
- How does the system behave under load — has a load test been done?
- Have error handling and fallback mechanisms been thought through?
- Is the cost side being monitored?
For example, imagine you are using LLMs in your application and you’ve embedded your OpenAI API key directly into the frontend.
In the beginning everything looks fine; the application works. But as soon as the app starts getting some traction, someone can grab the API key from the browser and start using your system for their own requests.
If you don’t have rate limiting or abuse protection either, thousands of API calls can pile up without you even noticing.
And most teams discover the problem not on a monitoring dashboard — but on the big invoice that arrives.
A Field as Critical as the Technical Side: Regulatory Compliance
Every application processing data under KVKK in Turkey, or GDPR in Europe, has rules to follow. The part most products built quickly with vibe coding miss is exactly this.
- Have KVKK/GDPR compliant data processing flows been defined?
- Are explicit consent and privacy notices designed correctly?
- Are users’ data deletion and portability rights supported technically?
- Is the lifecycle (data retention) of sensitive data being managed?
- Are personal data being kept in logs, and if so, how are they protected?
A data breach is not just a technical problem; it carries serious legal and reputational consequences.
AI Writes Code, It Doesn’t Take Responsibility. (For Now)
This is the point where I want to highlight the truth that deserves the most thought:
AI can build a system very quickly today. But here are some things it cannot do:
- It does not manage a data breach.
- It does not solve a security crisis.
- It does not pay the fines from regulators.
- It does not cover the costs caused by uncontrolled API usage.
- It does not bring a service back up at 3 a.m. when production goes down.
All of these are ultimately the responsibility of the developer. Or more accurately, the responsibility of engineering discipline.
Is Vibe Coding Bad?
No, absolutely not. As someone who has been actively doing vibe coding for the last two years, I use it actively and find it incredibly valuable for productivity. Prototypes that used to take weeks can now be stood up within a single day.
But here is the real point:
Vibe coding should mean “applying the right engineering practices faster”, not “turning off the controls”.
AI may be writing code for us. But reviewing that code, testing it, scanning it for security issues, and checking whether it aligns with architectural principles is still our responsibility.
The Right Approach: Build Fast, Manage with Discipline
A team that embraces vibe coding in a healthy way should follow this approach:
- Rapid prototyping: Turning ideas into working products in hours with AI.
- Architectural review: Looking at the system through an architect’s eyes before going to production.
- Automated security testing: Integrating SAST/DAST tools into CI/CD pipelines.
- Secret management: Isolating sensitive information from the codebase (Key Vault, Secrets Manager, etc.).
- Observability: Setting up logging, monitoring, and alerting from day one.
- Regulatory compliance: Building KVKK/GDPR-style regulations into the design phase.
- Continuous review: Reviewing every piece of AI-generated code through a senior developer or architect.
These steps may feel slow to you. But in the long run, the fastest path is always the one that’s set up correctly.
Conclusion
Vibe coding has shortened the path from idea to product more than ever in history. This is an extraordinary opportunity for independent developers, founders, and small teams. But this speed brings serious responsibility along with it.
Speed is an advantage. But indiscipline comes back as technical debt, security risk, and legal cost. And most of the time, it comes back much heavier than expected.
The teams that will succeed in the AI era won’t be the ones debating “AI or engineering?”. On the contrary, the ones who blend the two in the right proportion will make the difference.
Because the point is not just using AI; it is being able to manage the use of AI with engineering discipline.
See you in the next article…
Please feel free to comment and contact me with your questions.
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