SaaS built in Lovable or Cursor: How to secure code you didn't write yourself

Vibecoding has fundamentally changed SaaS development. With tools like Lovable and Cursor, development teams can now build functionality, integrations, and even entire products at a speed previously reserved for large organizations. It’s a huge boost to the pace of innovation, but it’s also changing the relationship between developers and their own code.

Because in many vibecoding-driven projects, the same situation arises: the product grows quickly, features are built over the weekend, users start testing – and everything works. But the question that often hangs in the air is: How much of this do we really understand at a detailed level?

That's not a problem in itself. It's a natural consequence of AI becoming a co-developer. But for a SaaS company to grow, meet customer demands, or enter enterprise business, it's crucial to know what 's been built, how it works, and what the security implications are .

When speed creates unknown surfaces

Developers using AI-generated code work more as architects and validators than as writers. That's a strength, less time on boilerplate, more time on problem solving, but it also means that some of the intuitive familiarity you otherwise get from writing code manually is lost.

This allows the attack surface to grow in unexpected ways: libraries are added automatically, authentication logic is simplified to “work”, APIs are generated with too broad access, or dependencies with known vulnerabilities sneak in. It’s not sabotage, it’s a side effect of speed.

In traditional projects, this is often discovered during development. In vibecoding projects, it is only discovered when someone actively looks for it.

Securing vibecoded SaaS is not about slowing down — it's about understanding

The important thing is not to check every line of code. The important thing is to create checkpoints that provide overview and security.

ISO 27001 is particularly useful in vibecoding environments because it is based on three fundamental pillars:

1. Identify what is worth protecting. In a SaaS, it is often identity flows, customer data, API keys, dependencies, and operating environment.

2. Understand the risks. Not hypothetical threats, but realistic ones: incorrect access controls, vulnerable libraries, invisible code logic, undetected API exits.

3. Create structured controls. So that even high development rates can be achieved with confidence — logging, code review, change management, configuration of cloud platforms.

   
   

The point is that the standard helps the team create predictability , not slow down innovation.

Gap analysis and penetration testing – the tools that provide clarity

In vibecoding solutions where no one has fully written all the code themselves, gap analysis becomes one of the most important steps. It helps the team see:

• what is missing to meet customers' security expectations,

• which parts of the code or environment need clarification,

• and where the structures are not aligned with the business's ambitions.

  
  

Penetration testing then serves the purpose of testing reality, not theory. It shows whether APIs hold up, whether access logic works, and whether AI-generated code has accidentally created openings that no one thought of. It's not an audit, it's a mapping of what the product actually does.

Risk analysis methods like TARA tie this together by prioritizing the risks that are business-critical. In rapid AI-driven development, it is often the difference between fixing the right thing on time and drowning in details.

Secure vibecoding is your competitive advantage

Lovable, Cursor, and other AI tools are not a threat to security. They are the way of the future of building SaaS. But to realize that potential requires a structured path to security, not more time, just more understanding.

SaaS products that combine the pace of vibecoding with a mature security structure stand stronger in customer dialogues, procurements and investment rounds. They grow faster, safer and more predictably.

In short: The important thing is not that you wrote all the code yourself, but that you know what it does.

Book a meeting with us and we can see how we can help your SaaS become secure.

More about our cybersecurity services here.

FAQ

1. Is AI-generated code less secure than manual code?

   Not necessarily. AI-generated code can be as secure as manual code, if not more secure, but the challenge lies in the overview. When developers have not written the entire code themselves, understanding of implementation, dependencies, and exception handling is reduced. That's why structured review and risk analysis are so important.

   
   

2. How do you know if code written in Lovable or Cursor is safe?

   It's not about the tool, it's about the process around it. Security is about understanding how functions are built, what access logic looks like, which libraries are used, and how data flows through the application. Code reviews, penetration testing, and a risk-based methodology provide the answer, not gut feelings.

   
   

3. Can vibecoding be used in products sold to enterprise customers?

   Absolutely. But enterprise customers expect clarity in risk management, documentation, security work and test results. As long as the development team works in a structured way, gap analysis, risk assessment, controls and recurring tests, vibecoding is not a hindrance but a strength.