The way companies build software has changed very quickly.
Today, a startup, small business, agency, or internal team can use AI tools to create web applications, dashboards, APIs, automations, chatbots, customer portals, and internal systems in much less time than before.
This speed is valuable. But there is one point many companies are still not treating seriously enough: many of these systems are being deployed without a proper security review.
AI helps write code, accelerate prototypes, and reduce development costs. However, when this process does not go through a technical security assessment, it can introduce silent vulnerabilities into the company’s environment.
The risk becomes even greater with the growth of vibe coding, where applications are created almost entirely through natural language prompts, often by people without deep experience in secure software development.
The growth of vibe coding in companies
Vibe coding has made it possible to turn an idea into a functional application in just a few hours.
A founder can build an MVP.
A sales team can create an automation.
An employee can generate a dashboard to track internal metrics.
A support team can create a chatbot.
A company can quickly launch a tool for customers.
The problem starts when these projects stop being experiments and begin to support real business processes.
An application initially created as a quick solution can later become connected to customer data, financial information, internal credentials, databases, corporate systems, or important operational workflows.
When this happens without security validation, the company starts depending on a system that may never have been designed to handle that level of exposure.
Functional code can still be vulnerable
Many AI-generated systems work well during normal use. The page loads, the login works, the API responds, and the main workflow delivers the expected result.
But security is not measured only by whether the system works.
A system can look correct to the user and still contain serious weaknesses from an attacker’s perspective.
Some of the most common issues in applications quickly built with AI include:
Exposed credentials in the code
APIs without strong authentication
Admin panels accessible from the internet
Authorization flaws between users
File uploads without proper validation
No protection against repeated attempts
Vulnerable dependencies
Insecure cloud configurations
Exposed databases
Errors revealing sensitive information
Lack of logs and monitoring
Excessive permissions in integrations
Unsafe use of sensitive data in AI prompts
These flaws are not always noticed during development because the main priority is usually to make the application work quickly.
For an attacker, each one of them can become an entry point.
The attack surface grows without the company noticing
Every new AI-built system adds new exposure points to the company’s digital environment.
It may be a login page, a public API, a webhook, an internal dashboard, a test server, a database integration, a cloud bucket, or an automation with elevated permissions.
Individually, each item may seem small.
Together, they significantly increase the company’s attack surface.
The critical point is that many companies do not have a clear inventory of what has been created, where it is hosted, who has access, what data flows through it, and which security controls have been applied.
This creates a dangerous situation: important systems running in production without the same level of protection expected from corporate applications.
A Canadian example: the Loblaw case
Canadian companies already face an active threat landscape, especially involving data exposure, unauthorized access, and compromised systems connected to third parties.
One recent example was Loblaw, one of Canada’s largest retail and pharmacy companies. In 2026, the company confirmed it was investigating a data breach after unusual activity was detected in part of its IT network. According to public reports, customer information such as names, phone numbers, and email addresses had been accessed by a third party, while passwords, credit card data, and health information were not reported as compromised.
Even when the impact appears limited, this type of exposure remains relevant.
Names, emails, and phone numbers can be used in phishing campaigns, social engineering, fraud attempts, impersonation, and targeted attacks against customers or employees.
This case shows an important point for companies accelerating development with AI: any system connected to the corporate environment can become part of the risk if it is not properly reviewed, monitored, and protected.
Now imagine this same scenario applied to several AI-generated internal applications, quickly built APIs, improvised admin panels, and automations connected to sensitive data.
That is how risk grows quietly.
The danger of prototypes becoming production systems
One of the biggest problems with AI-built projects is how quickly a prototype can become an official business tool.
The application starts as a test.
Then someone starts using it every day.
Then it receives real data.
Soon it starts supporting customers or internal teams.
By the time the company realizes it, that system has already become part of the operation.
But the security step was left behind.
This type of application often does not start with controls such as:
Strong authentication
Permission review
Access logs
Monitoring
Protection against API abuse
Secure secrets management
Validation against common attacks
Dependency review
Backup and recovery
Incident response planning
When a company moves this type of system into production without validation, agility becomes exposure.
AI applications bring additional security risks
Beyond traditional software vulnerabilities, systems that use AI can introduce specific risks.
Applications with language models, chatbots, autonomous agents, and intelligent integrations may be exposed to issues such as prompt manipulation, sensitive information leakage, improper execution of actions, excessive permissions, and unsafe use of corporate data.
Some questions need to be asked before putting this type of solution into production:
Can the application reveal sensitive data in its responses?
Can users manipulate the AI’s behavior?
Can the system access internal information without proper control?
Do integrations have excessive permissions?
Are prompts being stored with confidential data?
Is there human validation for critical actions?
Is there logging and monitoring of what the AI is doing?
These questions are essential because AI applications often connect to multiple systems, databases, and business workflows.
The more autonomy the application has, the greater the need for security controls.
What companies should review before using AI-generated software
Before using an AI-generated application in a real environment, companies should review at least the following areas:
Authentication controls
User permissions
API security
Admin panel exposure
Input validation
File upload handling
Protection against automated attacks
Credential management
Dependencies and libraries
Cloud configurations
Database access
Logs and monitoring
Error handling
Sensitive data protection
Third-party integrations
Prompt and AI response security
Backup and recovery
Incident response planning
This type of review does not need to slow down innovation.
It allows the company to keep using AI to move faster, but with much lower operational risk.
Fast development needs security built into the process
The trend is clear: more companies will use AI to create software, automate processes, and accelerate delivery.
This will increase productivity, but it will also increase the number of exposed systems, improvised integrations, and applications that never went through proper security validation.
Companies that understand this early will have an advantage.
They will be able to use AI more safely, reduce risks before incidents happen, and prevent tools created to accelerate the business from becoming entry points for attacks.
Security needs to be part of the process before the application becomes part of the operation.
This is especially important when the software was built quickly, with AI, with limited technical control, or without a security team involved in the project.
How LTH Cyber Security can help
LTH Cyber Security helps companies identify and reduce risks in modern applications, APIs, cloud environments, servers, internal systems, and projects developed with AI assistance.
Our work can include:
Web application security testing
API security testing
Penetration testing
Cloud configuration review
Vulnerability assessment
Attack surface analysis
Security review for AI-generated systems
Server and VPS hardening
Exposure analysis of public services
Practical remediation recommendations
The goal is to help companies benefit from the speed of AI without turning that speed into a vulnerability.
If your company is using AI to create applications, automations, APIs, portals, chatbots, or internal systems, LTH Cyber Security can help identify the risks before attackers do.
If your company is using AI to build applications, automations, APIs, portals, chatbots, or internal systems, this is the right time to review the security of those systems before they become an entry point for attackers.
LTH Cyber Security can help identify vulnerabilities, exposed services, insecure APIs, weak configurations, and risks introduced by AI-generated software.
Contact LTH Cyber Security to review your AI-built software and exposed digital infrastructure: https://lthcybersecurity.com/#contact
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