As a security engineer, I have recently immersed myself in Vibe Coding during my spare time. As part of my work, I have also studied dozens of apps built rapidly through Vibe Coding.
The more I see, the clearer one impression becomes: what truly concerns me is not the ability of AI to generate code, but the way it is changing the speed at which software comes into existence.
In the past, taking an application from an idea to a production launch required multiple stages involving product, design, development, testing, security, and operations. Now, someone without a development background can complete a data-analysis tool, an internal management page, a small program connected to enterprise APIs, or even a customer-facing web application in a matter of hours. For the first time, the ability to create software is spreading at scale beyond traditional development teams.
For the past decade and more, enterprise security has revolved around one central task: establishing boundaries. We divide network scopes with firewalls, restrict access through authentication, control data flows through permission systems, and safeguard software quality through code review.
AI, however, lets software be created far faster than traditional governance can keep up. Some boundaries that were once clear are becoming blurred. In the Vibe Coding era, the greatest risk is not AI itself, but the changes to security boundaries caused by employees using AI.
Today, I want to talk about four boundaries that are changing.
First: The Asset Boundary Is Becoming Harder to See
Traditional enterprise software assets usually follow a clear management path: requirements proposal → project approval → development and deployment → security review → formal launch. The security team knows which servers, applications, and databases exist, as well as which systems connect to external services.
Vibe Coding changes this process. Now, an employee may use AI to generate a tool in the morning, connect it to enterprise data in the afternoon, and deploy it to a personal cloud server at night. The entire process may bypass IT registration, security assessment, permission approval, and operations handover.
These unknown assets are known as Shadow IT. Employees can now create software on their own. The security team’s challenge has shifted from protecting existing assets to discovering new assets that keep appearing.
Second: The Data Boundary Is Easier to Breach
In the past, enterprises protected data through permission systems. What an employee could see was usually determined jointly by identity, role, permissions, and approval workflows.
AI-assisted programming has introduced new ways for data to move. For example, a business employee has permission to view customer data. To quickly build an analysis page, they might:
1. Export the data; 2. Ask AI to help generate an application; 3. Upload it to a platform that is convenient to access; 4. Share it with team members.
Data that was originally protected by permissions has entered a new application environment. More often than not, the issue is not malicious behavior; the creator has failed to realize that they have already moved the data across a security boundary.
Third: The Code Boundary Is Moving from Internal Repositories to the Open World
Code used to be an asset managed by development teams. Typically:
– It was stored in the company’s code repository; – access was controlled through permissions; – it was released through a CI/CD pipeline.
As AI programming tools become widespread, more and more people are encountering software code for the first time. A business employee may generate a small tool—a customer-management page + database connection + third-party API + automation script—and then upload it directly to a personal GitHub account simply to save the code.
It may expose:
– AWS Access Key; – database passwords; – API Tokens; – internal endpoint addresses; – business logic.
There is nothing wrong with GitHub itself. The real problem is that a growing amount of software assets is being managed by people who have not received software-security training.
Fourth: The Responsibility Boundary Is Becoming Blurred
In the past, software-development roles were relatively clear:
– Product handled requirements; – development handled implementation; – testing handled verification; – security handled risk control.
With the emergence of Vibe Coding, an analyst, operations staff member, or even salesperson can independently complete an application. If that application affects the business or leaks data, who is responsible?
For example, a member of a marketing team uses AI to create a customer-data analysis system. Later:
– The system connects to real customer information; – customers start using it; – data errors appear.
The creator is a business employee, and there may be no maintainer, but the security responsibility still exists.
The key question is not who wrote the code. More and more people will write code in the future. What matters is whether the person who creates a system understands the responsibility they are taking on.
In the AI Era, Security Needs Boundaries Redefined
AI has changed the speed at which security problems emerge. In the past, a vulnerability might take months to reach a production environment. Now, an application may be created, deployed, and circulated within a few hours.
In the future, enterprises need to rethink:
– Which AI tools may be used? – Which data may enter AI workflows? – Which applications require security review? – Who may create production systems? – How can unknown software assets be discovered?
Software development is moving from professional teams toward the broader public. Boundaries will not disappear on their own. If we do not deliberately redefine them, we may already be standing outside the boundary by the time we discover the risk.
*This article was written, proofread, formatted, and published by Li Yang’s AI assistant.