Across boardrooms and operational teams, conversations about AI are everywhere. There’s pressure to explore new tools, streamline tasks, and drive efficiency. But while most executives are still debating whether to formally adopt AI, their employees are already using it quietly, unofficially, and often without any security controls in place.

This is the rise of Shadow AI.

It’s not coming through procurement.

It’s coming through browser tabs.

The Newest Threat Is Self-Introduced

Shadow AI refers to the use of artificial intelligence tools by employees without approval, oversight, or even awareness from security teams. Unlike traditional shadow IT, this isn’t about rogue apps or unapproved devices. It’s about employees feeding sensitive data into public platforms, sometimes without realising the consequences.

Consider how easily this happens:

• A manager uses a chatbot to summarise confidential meeting notes.
• A junior analyst asks AI to help make sense of client data.
• An HR assistant drafts a warning letter using a generative tool.
• A developer pastes sections of proprietary code into a free prompt engine.

None of this feels like a breach in the moment. But it is. And most of it is happening under the radar.

Why This Matters Now

In many South African organisations, especially those navigating rapid digitisation, AI is being used long before it is being governed. The risk is not theoretical. It is practical. And it is already inside your environment.

Unlike phishing, ransomware, or endpoint threats, Shadow AI is not an external intrusion. It is an internal behaviour. One that exposes your organisation’s data, intellectual property, and compliance posture, often without triggering a single alert.

The biggest concern is not that people are experimenting with new tools.

It is that they are doing so without any understanding of the risk.

A Growing Gap Between Policy and Practice

Majority of AI tools in use today were never designed for secure enterprise environments. They are consumer-grade, browser-based, and often operate through cloud infrastructure outside your visibility. Once information is entered, you do not control where it goes, how it is stored, or who might be able to access it.

Yet for many teams, these tools have become indispensable. They reduce workload. Improve outputs. Save time. In environments where pressure is high and resources are stretched; they feel like a solution.

And that is where the danger lies. Convenience is often the biggest vulnerability in cybersecurity.

Security Isn’t Just About What Comes In. It’s About What’s Going Out.

Most cyber risk models focus on keeping threats out. Shadow AI flips that equation.

Now the risk is what your own people are putting into tools they do not understand, governed by policies that do not yet exist, supported by security teams that are not even aware it is happening.

This is not about control for the sake of it. It is about visibility, accountability, and data protection in a time where the lines between internal and external systems are becoming blurred.

Where to From Here?

Addressing Shadow AI does not require a ban. It requires structure.

Organisations need to build AI usage policies that are realistic, enforceable, and tailored to how people work.

That starts with education. Teams need to understand not just how AI works, but where its boundaries should be.

Security teams need to work alongside business units to define what responsible AI use looks like in practice. And leadership needs to ask better questions, not about whether AI is being used, but how, where, and with what consequences.

October is Cybersecurity Awareness Month.

If you have been waiting to introduce new awareness campaigns, training sessions, or internal reviews, now is the time to include AI in that conversation.

Because while your systems may be patched and your endpoints secured, the biggest risk may already be sitting in someone’s open browser tab.