AI doesn't compromise your security. Poor governance does.
Successful AI-assisted software development relies on integrating AI tools into existing engineering governance and security processes, rather than treating them as separate workflows that bypass established controls.
One of the biggest misconceptions around AI-assisted software development is that the technology itself creates security risks.
In reality, AI behaves like any other engineering tool. It amplifies the way software is already being built. Organisations with mature engineering practices tend to get better outcomes. Those without clear governance simply expose existing weaknesses faster.
The latest research reflects this pattern.
Veracode's 2025 GenAI Code Security Report found that 45% of AI-generated code samples failed security testing and introduced OWASP Top 10 vulnerabilities into the codebase. One of the most concerning findings was Cross-Site Scripting (XSS), where AI failed to produce a secure implementation in 86% of applicable scenarios.
Other studies have also shown that between one quarter and one third of AI-generated code suggestions contain Common Weakness Enumerations (CWEs), reinforcing the need for stronger security controls around AI-assisted development rather than less AI itself.
Why most AI adoption failures aren't AI failures
Every software delivery organisation is now being asked some version of the same question: where is AI actually paying off, and where is it just noise?
Read the whole article hereThose numbers shouldn't discourage organisations from adopting AI. They should encourage them to adopt it responsibly.
The problem isn't AI-generated code. The problem begins when AI-generated code is treated differently from every other contribution entering the software delivery lifecycle.
When AI bypasses architecture reviews, security testing, code reviews, or approval processes, organisations effectively create a second development workflow with fewer controls than the first. That's where risk emerges.
Security doesn't start with AI. It starts with engineering discipline.
Every organisation operating in a regulated industry already understands that security is built through repeatable processes, not individual tools.
The same principle applies to AI. Responsible AI adoption isn't about trusting the model to always generate secure code. It's about ensuring every AI-generated output is subject to the same engineering standards, validation, and oversight as human-written code.
At Vega IT, AI operates inside our existing delivery model rather than alongside it.
Our AI-assisted development follows the same ISO 27001-certified Information Security Management System that governs every software project we deliver. AI doesn't introduce a parallel workflow or bypass existing controls. It inherits the same security processes, approval mechanisms, auditability, and governance that our clients already expect from us.
For organisations operating in regulated industries, this extends to applicable compliance requirements such as PCI DSS and HIPAA, ensuring that AI tooling is evaluated against the same security and data-handling standards as any other technology introduced into the delivery environment.
AI tools should earn trust before they enter the workflow
One of the most common mistakes organisations make is allowing engineers to choose AI tools individually without establishing clear governance first.
We take the opposite approach. Every AI tool goes through the same evaluation process as any other technology entering our engineering ecosystem. Security, privacy, data handling practices, and compliance requirements are assessed before approval is granted.
Access is equally controlled. Engineers don't automatically receive AI tooling simply because it's available. They first complete internal AI awareness and enablement programmes that cover responsible usage, security considerations, known limitations, and project-specific guardrails.
AI should adapt to your governance model – not the other way around
Every client has a different appetite for AI.
Some encourage widespread adoption. Others impose strict limitations on where AI can be used, what data can be processed, or which tools are permitted.
Those differences shouldn't require compromises.
Before introducing AI into any engagement, we align our approach with the client's internal AI policies and governance framework. If the client's requirements are stricter than our own internal standards, those requirements take precedence.
This ensures AI becomes part of the client's existing governance model rather than creating an entirely new one.
