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Although it may sound like something out of a ’70s self-help book, the term “vibe coding” was just coined earlier this year, and since then, the idea has been spreading fast. It refers to people with little or no programming experience using AI tools like Replit, Cursor, ChatGPT, Claude or GitHub Copilot to build real software and complete AI SaaS Platforms, just by describing what they want using natural language in a conversational tone.
Imagine you’re a nontechnical founder with a great idea for a product. Once upon a time—say, a year ago—you’d have to find a developer, figure out a budget, maybe even offshore some work to build a prototype. Now you can just tell an AI, “I want a website that does X, Y and Z,” and voila—there’s your working code and usable, sellable product.
For early-stage validation, this can be a game-changer. But as cool as it sounds, vibe coding comes with real risks. And if you don’t know where those risks are hiding, you could end up in a world of trouble.
Where Vibe Coding Works—And Doesn’t
I’ve seen firsthand how powerful vibe coding can be for prototyping. If you’re still trying to figure out your ideal customer profile or whether your product idea actually has legs to build traction, using AI to come up with a fast minimum sellable prototype can be very effective. You get something tangible into users’ hands early and can make informed decisions without sinking tens of thousands of dollars into dev work.
But that doesn’t mean you should trust AI from start to finish, especially if you’re working in areas that deal with sensitive data, or if you’re in a regulated industry like healthcare or finance.
Here’s why: Large language models weren’t trained on clean, secure, regulatory-compliant code. They were trained on code that’s already out there on the internet. Some of it is good, but a lot of it is sloppy, outdated or full of vulnerabilities. On top of that, by definition, most people using vibe coding don’t fully understand the code that’s being generated. It’s like building a house based on blueprints gathered from all over the internet, and you’re not an architect.
This dramatically increases the risk of security and privacy breaches as well as regulatory and compliance violations. We’re already seeing this. Startups that grew fast using vibe coding are starting to appear in the headlines for the wrong reasons: hacked APIs, exposed user data, major privacy issues.
Asking AI to identify and fix security, privacy, and architectural issues is like asking a 10-year-old to drive 70 MPH on the freeway when their only driving experience is GTA and Asphalt Unite.
Don’t Code On Autopilot
So, what’s the better approach? Think of vibe coding like lane keeping assist: It helps, but you still have to keep your hands on the wheel. That means involving a human expert—someone who knows how to check architecture, security and scalability—instead of relying 100% on anything AI-generated for a real, user-facing product. In the end, if you want a production-quality product, you still need a human in the driver’s seat.
People assume AI-generated code is good to go because it “works.” But functioning and being secure and able to scale are two very different things. Every piece of software should go through proper review and testing, especially for things like input validation, authentication and how data is stored or transferred.
If your product touches anything sensitive, such as financial data, intellectual property or trade secrets, you have to be extra careful. AI tools often send data back to third-party servers, which means you might be exposing private or proprietary info without even knowing it.
There’s a reason we don’t have “vibe medicine” or “vibe finance.” You wouldn’t go to ChatGPT for a court defense (at least not yet). The same logic applies to software that handles real people’s data or money.
Doing It Right
How can you leverage the benefits of vibe coding in a smart way? First, by all means, use it for what it’s best at: building early prototypes. If you’re not sure your idea will work, vibe coding is a great way to get to a proof of concept. Test your assumptions. Show it to users. But don’t scale from there without help.
Second, loop in technical advisors early. If you can’t read the code, find someone who can. There are even services from Amazon (AWS), Google (GCP) and Microsoft that help you vet your architecture. AWS, for example, has startup programs that include free credits and partner assessments to flag security or scaling problems.
Third, you can use automated tools like OWASP ZAP, Snyk or SonarQube to scan the AI-generated code for known vulnerabilities. These tools aren’t perfect, but they’ll increase your odds of catching obvious problems before users (or hackers) do. Create your own CI/CD pipelines to consistently scan your code.
Vibe coding is here, it’s easy and it’s definitely useful. But it’s not a free pass to skip over everything that makes software trustworthy, secure and scalable. Right now, AI is great at saving time but not at making decisions about privacy, ethics or architecture.
Think of vibe coding as a driver assist feature, not a driverless autopilot. You still need to know where you’re going, and you need some experienced human input along the way. Otherwise, you probably won’t end up where you want to go—if you don’t crash first.
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