What the Rise of AI Means for Data Security

ARTIFICIAL INTELLIGENCE IS CHANGING HOW BUSINESSES WORK.

It helps people analyze information, write reports, and finish everyday tasks faster. What began as an emerging technology has quickly become part of daily operations across nearly every industry.

A recent data security report shows that this progress comes with new risks. AI tools are being used widely, but many companies do not have clear policies on how to manage them. Employees turn to these tools to save time and improve productivity, often without realizing they may be sharing sensitive information in the process. Once information is entered into an external AI platform, the company loses visibility into where it goes or how it’s stored. To help clients, prospects, and others, Koltiv has summarized the key details below.

What Is Data Exfiltration?

When information leaves a company’s secure systems without permission, it is known as data exfiltration. In simple terms, it means company data has been moved, copied, or shared somewhere it does not belong. Sometimes this happens through a cyberattack, but more often it occurs by accident.

A common example is an employee who pastes a client name or financial summary into an AI tool to get help with writing or analysis. Once that information is entered, it can be stored or processed outside the company’s network. Even if the employee deletes it, the data may still exist in an external system.

This type of exposure can lead to real consequences, from privacy violations to compliance issues. Many employees do not intend to create risks, but they may not understand how AI platforms handle the information they receive.

AI as a Top Threat

The same data security report found that AI has become one of the main ways company data leaves secure systems. Nearly half of all employees now use generative AI tools, and these platforms account for about 10% of all business application activity. That puts AI on the same level as email, file sharing, and other core workplace tools.

The report also found that about 40% of files uploaded into AI tools contain personal or financial information. That can include names, payment details, or internal documents that should stay within company systems. Once entered into an AI platform, the data may be stored, copied, or used to train the model itself.

Traditional security programs were not built for this type of risk. They can detect phishing emails or stop unauthorized file transfers, but they do not track what employees type into AI chat windows. As a result, sensitive information can be shared without setting off an alert.

Additionally, many employees report using AI tools through personal accounts that aren’t connected to the company’s security systems. When that happens, the organization has no way to track who is using the tools or what information is being shared.

Why This Matters

This is not a problem just for the tech team. There are regulations that hold organizations accountable for how they manage data, even with AI rapidly changing the threat environment. It’s truly an enterprise-wide issue that can affect compliance, privacy, and client trust. 

A single instance of information exposure can trigger data breach reporting or lead to questions about internal controls. In addition to potential legal and financial consequences, there is also the impact on reputation to consider. 

Risk Management and Next Steps

Protecting data in the age of AI is really about control and awareness. Companies don’t necessarily need to ban AI outright, but they do need to put structure and safeguards around how it’s used. Here’s what that looks like in practice:

• Adopt a policy — Most organizations still don’t have a clear policy for AI use. In fact, a recent survey found that only 36% have established any formal guidelines. Even a short-written policy can make a big difference when it’s built into existing governance frameworks. Explain what kind of information can go into AI tools and what should never leave secure network systems.
• Use secure platforms — Public AI tools are convenient, but enterprise versions are more secure. They can turn off data retention, isolate information from public training, and give administrators visibility into who’s using the tool.
• Manage access — Consider single sign-on and multifactor authentication (MFA) for any AI system connected to the business network. This can prevent unauthorized use and flag unusual activity.
• Provide training — Most data breaches happen by accident. Employees may paste something into a chat window without realizing it’s sensitive information. Ongoing training helps staff members understand how AI works and how to keep information from leaving the organization. 
• Update monitoring tools — Traditional data loss prevention systems weren’t built to watch AI traffic. Modern solutions can spot when information is being uploaded to AI sites and alert administrators. 

Contact Us

AI is rapidly changing the nature of security threats. It can make work faster and more efficient, but it’s important to realize that it also creates new risks. Companies that plan ahead can get the benefits of AI without putting critical data in danger of a breach. If you have questions about your company’s approach to AI and data security, Koltiv can help.

For additional information, call 855.723.3628 or click here to contact us. We are excited to speak with you soon.