Deploying AI-Generated Apps Securely to the Cloud: What Companies Must Watch

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AI tools such as Lovable, Bolt, v0, Replit or Cursor have changed software development. An idea turns into a working app within hours, often without a single line written by hand. The demo runs, login works, the database is connected. The reflex is understandable: deploy quickly and go live.

That is exactly where the problem lies. A running demo is not a production-ready system. AI models optimise for code that works, not for code that is secure. A 2025 Veracode study found that around 45 percent of the code produced by more than 100 language models contained security flaws from the OWASP Top 10 class, and that newer or larger models did not perform any better (Veracode GenAI Code Security Report). The risk is not a transitional phenomenon but structural.

This article outlines what companies must watch when they want to bring an AI-generated app to the cloud securely: from access control to secrets and the database, through to deployment hardening and GDPR.

Table of Contents

• Why AI Code Is a Security Risk
• Authentication and Access Control
• Secrets and API Keys
• The Database: The Supabase RLS Problem
• Dependencies and the Supply Chain
• Hardening the Deployment
• GDPR and Data Location
• Before Go-Live: The Checklist
• Our Approach at WZ-IT
• Further Reading
• Sources

Why AI Code Is a Security Risk

The core problem is not that AI writes bad code. It writes plausible code that fulfils the intended function. What it systematically underestimates are the things that do not show up in the demo: what happens when an attacker calls the API directly? What if someone manipulates the user ID in the request? What if an input is not what the form expects?

The Veracode analysis shows the pattern clearly: for some vulnerability classes such as cross-site scripting, the failure rate exceeded 80 percent. A second risk compounds it: anyone who has an app fully generated often does not understand in detail what the code does. That removes the very basis for spotting security flaws in the first place.

For companies this means an AI-generated app is an excellent starting point, but it belongs on the test bench before going into production. The following sections are the areas where most problems hide.

Authentication and Access Control

In the OWASP Top 10, Broken Access Control ranks first. This is exactly where AI-generated apps fail most often. Typical patterns:

• The interface hides a button, but the underlying API does not check the permission. Whoever calls the API endpoint directly gets through anyway.
• A user can retrieve other users' data by changing an ID in the request.
• Admin functions are not secured server-side but merely hidden in the frontend.

The rule: access control always belongs on the server, never only in the frontend. Every endpoint must check whether the authenticated user is allowed to perform the requested action. AI-generated code often implements this only halfway, because the demo works without a complete check.

Secrets and API Keys

The second classic is credentials in the wrong place. AI tools like to put API keys, database passwords or tokens wherever they are needed at the moment, and that is frequently the frontend code or a config file that ends up in the repository.

Anything that reaches the browser is public. An API key in the frontend bundle can be read out with developer tools. A secret committed to the Git history stays there, even if the next version removes it. The principles:

• Secrets belong in server-side environment variables, never in the frontend and never in the repository.
• Separate public and secret keys clearly. With services like Supabase the anon key is deliberately public, while the service-role key is strictly secret.
• Accidentally exposed keys get rotated, not just deleted.

The Database: The Supabase RLS Problem

Many AI-generated apps use Supabase or a similar backend-as-a-service provider, because it lets you wire up a database backend quickly. That is convenient, but it has a sharp edge: Row Level Security (RLS).

RLS controls per data row who may read or write what. According to the Supabase documentation, RLS must be enabled for every table in the public schema. The catch: tables created via the graphical table editor have RLS enabled by default, but tables created via raw SQL do not. And that is exactly what AI-generated code likes to do, without setting the policies.

The consequence is drastic: without RLS enabled, anyone who knows the public anon key has full read and write access to the affected tables. The anon key sits in the frontend, so it is visible to everyone. A vibe-coded app with RLS disabled is effectively an open database on the internet. Before every go-live, therefore: check RLS on all tables, write policies, test with different user roles.

Dependencies and the Supply Chain

AI-generated code pulls in third-party packages, and that creates a new risk. Language models occasionally suggest package names that do not exist at all. Attackers exploit this by registering these hallucinated names in npm, PyPI and other registries in advance and filling them with malicious code. The pattern has a name: slopsquatting.

A study presented at USENIX Security 2025 analysed 2.23 million code samples and found that open-source models invented non-existent packages in around 21.7 percent of cases, commercial models in around 5.2 percent. Particularly critical: 43 percent of these hallucinated names recurred across repeated queries, making them predictable and therefore deliberately attackable (Snyk: Slopsquatting).

On top comes the usual problem of outdated packages with known vulnerabilities. Both are addressed the same way: check dependencies against the official registries and against known CVEs before deployment, and establish ongoing CVE monitoring in operation that does not go stale after four weeks.

Hardening the Deployment

Even if the application code is clean, the deployment environment decides security. The most important points:

• TLS everywhere. No production service runs without encryption. A reverse proxy with automatic certificates is standard.
• Rate limiting. Without limits, login endpoints and expensive API calls are open to abuse and brute force.
• Set CORS and security headers properly. Correct origin restrictions and headers like Content-Security-Policy prevent a whole class of attacks.
• No open admin or debug endpoints. What was convenient in development must not be reachable in production.
• Logging and monitoring. Without logging, no one notices that something is happening. Security Logging and Monitoring Failures are their own item in the OWASP Top 10.

For running an app self-hosted, Coolify offers a sovereign deployment platform that provides TLS, reverse proxy and database provisioning from a single source. How to set an app up cleanly with it is shown in our article on Coolify v4.0.0.

GDPR and Data Location

Cloud deployment adds a layer that purely technical checklists often omit: data protection. Many AI tools deploy to US platforms by default. As soon as personal data is processed, that becomes a concern.

A data location in the EU is the basic prerequisite, but not sufficient on its own. It also requires the data processing agreement, clean access control, logging and the exclusion of third-country access. An app on European infrastructure considerably simplifies the data protection assessment compared to US platforms that fall under the CLOUD Act. To be on the safe side, plan the deployment from the start with GDPR compliance and data sovereignty in mind.

Before Go-Live: The Checklist

Before an AI-generated app goes live, these points should be checked. A structured, independent review surfaces exactly the gaps that get missed when building in-house (source):

1. Access control checked server-side for every endpoint.
2. Secrets removed from frontend and repository, exposed keys rotated.
3. Row Level Security enabled on all database tables and tested with multiple roles.
4. Inputs validated server-side, not just in the form.
5. Dependencies checked against known CVEs and for existing package names.
6. Deployment hardened: TLS, rate limiting, CORS, security headers, no open admin endpoints.
7. Logging and monitoring active, CVE monitoring set up for ongoing operations.
8. Data location and GDPR clarified.

Our Approach at WZ-IT

An AI-generated app is a good starting point, but the path from demo to a robust production system is handwork. Here is how we proceed:

1. Audit. We check the app against the points above: access control, secrets, RLS, validation, dependencies and deployment. The result is a concrete, prioritised list.

2. Hardening. We close the gaps found, separate secrets cleanly, enforce RLS and server-side validation, update dependencies and harden the environment.

3. Sovereign deployment. We bring the app onto European infrastructure, with TLS, reverse proxy, backups and monitoring. Whether self-hosted on Coolify or in a managed setup.

4. Managed operations. Updates, patch management and CVE monitoring in ongoing operation, so the app is still secure six months from now.

We bundle the entire path from vibe-coded app to production in our prototype-to-production support, complemented by AI software development for everything beyond that.

Further Reading

• Lovable vs Bolt vs v0: An Honest Comparison 2026 - which AI tool for what
• Coolify v4.0.0: Sovereign Deployment Platform - deploy the app cleanly
• Deploying OpenClaw Securely: Security Hardening Guide - hardening by concrete example
• CVE Monitoring for Self-Hosted Software - why patch management is mandatory
• Prototype to Production at WZ-IT - making vibe-coded apps production-ready

Built an AI app but unsure about go-live? We audit access control, secrets, RLS and deployment, harden the app and operate it sovereignly on European infrastructure. Schedule an intro call

As of June 2026. This article is not legal advice. For concrete data protection and compliance questions, consult data protection and legal counsel.

Sources

• Veracode: GenAI Code Security Report 2025
• OWASP Top 10
• OWASP Top 10 for LLM Applications 2025
• Supabase: Row Level Security
• Supabase: Securing your API
• Snyk: Slopsquatting and AI-hallucinated packages
• Cloud Security Alliance: Slopsquatting and the AI supply chain
• Have your AI app reviewed before deployment