Vibe coding is when you use AI to build you an application, normally done by non-developers with functional knowledge of a business problem and the AI does the rest, takes the requirements and develops a solution prototype. As these get more popular it is becoming a trend for businesses to start using these vibe-coded solutions and roll them out across the business without fully understanding the risks involved.
As a certified Microsoft Solutions Partner and .NET development provider, Bridgeall helps organisations build secure, sustainable digital solutions. Here is why reliance on vibe coding poses significant risks for enterprise software estates, and what leadership teams must consider before scaling AI-generated applications.
The Hidden Risks of Enterprise Vibe Coding
- Architectural Fragility and Moving to Production
AI models generate code based on prompts and the context available to them but may lack a holistic view of enterprise architecture. On a local device, a vibe-coded app operates in a vacuum with static test data and single-user access. What works seamlessly on a developer’s laptop can quickly become a costly, unmaintainable risk when exposed to enterprise workloads, security requirements, and long-term compliance standards.
Deploying that application into a production cloud environment like Microsoft Azure immediately exposes structural gaps:
- Skillset – Anyone with the right access to AI can vibe code a solution that runs on their PC. But having the skills and experience to deploy something like this into the cloud and run it across a company is very much a developer task and so this becomes one of the first real blockers.
- Deployment Problems: Vibe coded apps often do not include the setup needed to test, update and deploy them automatically to the cloud.
- Security and Access Risks: Passwords may be stored directly in the code, API access may be too broad, and the app may not connect to company sign-in systems such as Microsoft Entra ID. This can allow unauthorised users to gain access.
- Slow Performance at Scale: AI-generated database code is often not designed for large numbers of users. If many employees use the app at the same time, it can use too much memory, overload the database and become slow or unreliable.
- The Maintenance Nightmare and Code Decay
Software engineering is rarely about the initial build; the vast majority of application lifecycle costs reside in long-term maintenance. When the vibe code application breaks, who fixes it, when no one knows how it works? Add in the fact that the code is often build randomly this can make it hard for even software developers to easily identify the issue.
When you combine this with moving requirements and a need to make even small changes things can get quite challenging quickly.
Vibe coded software typically suffers from high technical debt from day one:
- Spaghetti Architecture: AI code generation tends to stack quick patches on top of existing functions rather than refactoring the underlying logic cleanly. This creates bloated, repetitive, and unreadable codebases.
- No Authoritative Context: Because no human engineer authored the logic or documented the system boundaries, internal IT teams cannot easily troubleshoot bugs, apply patches, or implement feature requests without risking unforeseen cascade failures.
- Lack of Vendor Updates: Traditional enterprise software benefits from clear vendor roadmaps, security advisories, and framework patch cycles. A vibe-coded app has no vendor support. If an underlying runtime or dependency deprecates, your internal teams are left supporting a custom codebase that nobody fully understands.
- Exploding AI Token Costs
Many vibe-coded applications rely heavily on continuous, real-time calls to Large Language Model (LLM) APIs behind the scenes to perform tasks that should be handled by standard, deterministic code logic (such as string formatting, data transformation, or basic validation).
While individual API calls cost fractions of a penny during local testing, running LLM-dependent logic across an entire workforce causes token consumption to compound exponentially.
Without token caching, response throttling, or architectural cost governance, organisations face unpredictable monthly cloud invoices that quickly erode the perceived cost savings of rapid AI prototyping. Combine this with the fact that every time you want to make even a small change it effectively rewrites the application which then uses a number of tokens.
Bridging the Gap: Governed Low-Code and Enterprise Engineering
Rapid prototyping using AI is a valuable component of modern digital transformation, but it must be governed by proven software engineering frameworks.
Rather than allowing unvetted, vibe-coded applications to proliferate across your estate, forward-thinking organisations establish clear guardrails:
- Leveraging the Microsoft Power Platform: For rapid internal tool creation, tools like Power Apps and Power Automate provide low-code agility while enforcing built-in enterprise governance, Microsoft Entra ID security, and automated lifecycle management out of the box.
- Structured Pro-Code Architecture: For bespoke, line-of-business applications, custom software engineering on .NET 10 and Azure ensures your applications are architected for enterprise concurrency, strict data compliance (UK GDPR), and seamless long-term maintenance.
- AI Governance and Token Optimisation: Structuring AI integration through managed services like Azure OpenAI Service ensures data privacy, predictable token budgeting, and enterprise SLA protection.
- Proof of concepts to production: Allowing your users to vibe code, solve problems and make improvements is key, but identifying the key solutions and best uses and then taking control of these to launch them into production by a developer could solve a number of issues.
Partner with Bridgeall for AI Application development
AI tools should accelerate software development, not replace sound architectural principles, security standards, and governance models.
At Bridgeall we are a .NET development partner that has integrated AI into our development services. Whether you need to assess an existing application estate or architect a new cloud solution, we provide the technical expertise required for sustained enterprise success.
