Generative AI is everywhere, and consumer-facing tools like ChatGPT, Copilot, and Gemini have thoroughly introduced themselves to the public at large. But as exciting as it is, the technology is still in its relative infancy. Governments around the world are still grappling with how to effectively regulate both the use and the development of generative AI, even as its capabilities multiply on a seemingly daily basis.
As with any new technology, AI carries an inherent degree of risk. For example, it’s important to know how companies train their models. What data was used and where did it come from? Does it carry any inherent biases? Was it gathered in a manner consistent with existing regulations like GDPR or CPRA? Without knowing the answers to these questions, it’s difficult to deploy AI in a safe and effective manner.
These concerns have led some businesses to shy away from AI-based technology — or at least limit its usage. But as AI tools become increasingly accessible, individual employees are seeking them out on their own to streamline their work and make their own lives easier. They’re doing so either with or without the blessings of their employers. That means organizations may not even know which AI solutions their employees are using, let alone how they’re using them.
This unofficial implementation creates significant risk, and today’s businesses can’t afford to ignore it. They need a plan in place to help them address and outsmart the unknown.
What Is Shadow AI?
The term “shadow AI” refers to employees’ use of AI-based tools in their work without the knowledge or consent of their organizations. This unsanctioned usage can introduce risk to a business in a variety of ways.
The Rise — and Risk — of Shadow AI
“Shadow AI” is a term that refers to AI-based tools employees use without the knowledge or blessing of their employers. It represents a relatively recent concern. Though AI capabilities have been around for some time, they’ve largely been restricted to more business-oriented use cases. But today’s consumer-facing AI solutions have put impressive new tools in the hands of everyone.
The same AI capabilities once used to analyze data or streamline automated functions are now helping employees draft emails, summarize reports and engage in other timesaving activities. In some ways, this is good. After all, if these tools help employees be more efficient and productive, that’s a good thing, right?
The reality is more complicated, however. When it comes to AI, what you get out of it depends on what you put into it. An employee using generic prompts to get ChatGPT to draft email responses probably isn’t posing much of a risk. But what happens when employees start asking ChatGPT to summarize lengthy — and confidential — client contracts? What happens when they start asking Copilot to review proprietary code for an upcoming application? Or using it to scrub customer data for potential leads? Right now, there are a wide range of “free” generative AI solutions, but they come at a cost.
That cost is risk. Most businesses would probably prefer to keep their client contracts, proprietary code and customer data tightly under wraps. If employees are inputting that data into generative AI tools, however, the risk of exposure is significant. When it comes to generative AI, data leakage is a real problem. In fact, one recent study indicated that as many as 55 percent of data loss protection events involved users sharing personally identifiable data with generative AI sites.
With so few rules in place, it’s hard to know what sites do with that data, how well they protect it and whether they use it to train their own AI models. The last thing a business wants is the code for their innovative new software being leaked to a competitor thanks to a poorly secured AI tool.
Help Employees Use AI More Safely
None of this is to say that AI-based solutions are bad. Of course they’re not. But it’s important for businesses to weigh the benefits against the risks, and that means having an eyes-wide-open approach to AI rather than sticking your head in the sand. Businesses need to know how their employees are using these tools, and they need to have policies in place to effectively govern that usage.
It doesn’t matter whether the company embraces AI on an institutional level. AI tools are here. They’re easy to use, and they’re increasingly accessible. That means AI governance is no longer optional — it’s essential.
That also means creating a culture where AI is accepted (or even encouraged) when it is appropriate. Attempting to ban AI tools won’t stop employees from using them; it will just make them hide their use. In contrast, having a governance structure helps put a plan in place. It provides guidelines that let employees know what is and is not acceptable and why.
Of course, it’s important to have enforcement mechanisms in place, such as solutions that prevent employees from sharing sensitive or confidential data. That doesn’t mean employees who slip up should be punished (unless they do so repeatedly), but it helps security teams understand where data leakage may be occurring and put countermeasures in place.
As AI becomes increasingly ubiquitous, most organizations will eventually embrace the technology to one degree or another. But even those that have yet to actively incorporate AI into official business processes need a plan to govern its usage. Turning a blind eye to shadow AI can result in data leakage or worse. Today’s organizations cannot afford to be caught by surprise.
