AI Is Already Being Used in Your Organization. But Do You Have It Under Control?

In this episode of Business Tech Talks powered by BlueSoft, we explore the key risks and challenges surrounding Shadow AI and corporate data security in a world where artificial intelligence is widely available. We look at the risk of confidential data leaks, the responsibility of leadership to put the right policies in place, and the practical steps companies can take to bring AI use under control — from employee education and data classification to the use of tools like DLP. BlueSoft expert Diana Kisiała explains how to build a culture of security and provide employees with approved solutions that improve efficiency without putting the organization’s reputation or finances at risk. Below, you’ll find a detailed summary of the episode transcript.

What is Shadow AI, and why is it a risk?

Shadow AI refers to employees using AI tools without the organization’s knowledge or approval. The biggest risk is that public LLMs (such as the free version of ChatGPT) may learn from the data they are trained on, potentially exposing sensitive information outside the company. Real-world examples, such as Samsung’s source code leak or Amazon’s AI-generated responses resembling confidential internal documents, show that the consequences can include multimillion-dollar losses and serious reputational damage.

Who is responsible for data security?

The core principle is simple: responsibility for data security always lies with the company’s leadership and the organization as the data controller. While an employee may directly cause an incident, the root problem is often a lack of awareness, unclear procedures, or a lack of access to professional, company-approved tools. Organizations must also consider severe financial penalties under regulations such as GDPR and DORA, as well as the risk of losing customers if trust is broken.

Why do employees take the risk of leaking data? 

In most cases, employees are simply trying to work faster and more efficiently. AI tools are already being used across nearly every department — from marketing teams creating content, to HR teams reviewing CVs, to legal teams translating contracts. The problem begins when companies fail to respond to these needs and do not provide safe alternatives, leaving employees to rely on private accounts and free, unsecured tools.

Is banning AI the answer?

Experts agree: no. Bans do not work because technological change is happening too quickly and is impossible to stop. Instead of trying to block AI, organizations should focus on putting safe rules and structures around its use. That means giving employees access to approved enterprise tools — such as corporate licenses or internal AI models — with contractual guarantees that company data will not be used to train public systems.

How DLP tools help protect sensitive information

One technical solution that can support data security is DLP (Data Loss Prevention). These tools act as intelligent filters that monitor activity on company devices and can block the transfer of sensitive data — such as personal ID numbers or documents marked confidential — or send alerts to the security team. Implementing DLP effectively, however, requires both budget and executive support, as well as close cooperation with IT.

Practical steps for implementing AI safely

According to the expert, a safe AI implementation process should include:

• Education and culture – Train employees and build trust so they feel comfortable reporting incidents before they become public.
• Needs analysis – Survey employees to understand which processes they want to automate.
• Updated procedures – Create policies aligned with GDPR, DORA, and the upcoming AI Act.
• Data classification – Introduce clear confidentiality levels (Public, Internal, Sensitive, Top Secret) so employees know what can and cannot be shared in AI tools.

Summary

Addressing Shadow AI is not about imposing strict bans. It’s about actively equipping employees with secure, approved tools that do not use confidential data to train public models. The key is to combine a solid understanding of employee needs, protective technologies such as DLP, and a workplace culture built on education, trust, and openness in reporting incidents. With clear data classification and tools that ensure full accountability, organizations can safely unlock the value of AI while staying compliant with regulations such as GDPR, DORA, and the upcoming AI Act.