AI Can Build It. But Is It Ready for Your Business?
Understanding Vibe Coding Security Risks
Employees are using AI tools to improve their productivity, from creating applications to generating code and automating repetitive tasks. These solutions can address real workflow problems quickly, but a tool that works for its creator is not necessarily ready for the rest of the business. Where the workforce sees speed and productivity, leadership must consider the broader AI risks for businesses, including security vulnerabilities and gaps in data governance.
Before an employee-built solution connects to company systems or handles sensitive information, leadership needs to ask: Is it secure, reliable and ready for business use?
Secure AI implementation requires reviewing how the solution accesses data, interacts with existing technology and will be maintained after deployment.
When an Employee Builds a Useful Tool with AI
AI-assisted development allows people to create applications and workflows with help from AI-generated code. One approach, known as vibe coding, relies heavily on plain-language prompts to an AI model to generate and revise the code, sometimes with limited understanding of that underlying code. Vibe coding security risks highlight why these solutions need technical review before business use.
Consider an employee who uses Claude or ChatGPT to build an automated onboarding tool or to share information across business applications. When a new client record is created, the AI-built tool checks whether required documents have been received and flags missing information.
For the employee, this solves a real workflow problem. For leadership, it raises additional concerns, such as the risk of compromising sensitive data.
- Which client records can the tool access?
- Where does it store information?
- What happens if it misses a document or stops running?
To turn a useful idea into a solution the business can use confidently, leadership needs to provide direction to ensure the solution meets the organization’s security policies, governance standards and compliance requirements.
Why a Working Prototype Needs Review
An employee building a tool with AI may focus primarily on whether it completes the task. Confirming that the code works and the onboarding checklist runs, however, does not establish whether the solution protects client information, uses appropriate permissions during database queries or can recover from a failure.
Even if an AI-built solution operates without AI after development, the resulting application or workflow needs review before it connects to business systems. The review should address the security implications of the solution itself, its connections and the environment in which it will run.
Involve IT and security teams during AI code generation and development whenever possible. Use company-approved AI tools and avoid entering client information, passwords or API keys into prompts. Use synthetic or appropriately sanitized test data during development. If a prototype already exists, have it reviewed before expanding access or relying on it for daily operations.
Security Best Practices: What a Secure AI Implementation Review Should Cover
To reduce security risks, review and test the solution before deployment and establish controls around what it can access and do. Using the onboarding tool example, the review should cover the following areas:
- Data handling: What information does the onboarding tool access, and where is it sent, stored or logged? Establish appropriate retention, deletion and backup requirements that align with your organization’s approach to data integrity and governance.
- Access and credentials: Which accounts and permissions does it need? Are proper access controls in place, and are the credentials used to connect applications protected? Limit access to the records and actions required.
- Application security: How does it handle incomplete records, unexpected user inputs and errors? Review the code and components for security vulnerabilities that could expose client documents or other sensitive data.
- Infrastructure fit: Where will the tool run, and how will it connect to existing applications? Confirm that those connections fit the organization’s network, hosting and security controls.
- Reliability & maintenance: What happens if a connected application changes or the checklist stops running? Establish how failures are detected, who receives an alert and how staff can continue onboarding while the issue is resolved.
Assign Ownership Before Deployment
AI can suggest these safeguards, of course, but qualified people need to approve, implement and verify them. Business leadership should identify who authorizes the solution for use, who maintains it and who responds when something goes wrong.
For the onboarding tool example, that means assigning responsibility for its technical maintenance and for confirming that the onboarding process continues to work as intended. The employee who created the prototype may understand the workflow well, but should not automatically become its sole technical support resource.
Document those responsibilities before deployment, along with the process for reviewing changes to the tool, its permissions or its connected applications.
Move From Useful Idea to Business-Ready Solution
An employee-built onboarding tool is just one example of how applications built with AI-generated code can save time and reduce manual follow-up. Realizing that value requires confidence that it handles client information appropriately, works within the existing technology environment and has someone responsible for keeping it running.
If your team has already built a solution with AI, Fairdinkum’s engineers can review it, identify security considerations and infrastructure gaps, and recommend a practical path toward business use. If you have an idea but have not started building, Fairdinkum can design and develop the solution with security, reliability and maintenance considered from the beginning.