By: Anand Oswal,

SVP & GM of Network Security, Palo Alto Networks

The adoption of unsanctioned GenAI applications can lead to a broad

range of cybersecurity issues, from data leakage to malware. That’s

because your company doesn't know who is using what apps, what

sensitive information is going into them, and what’s happening to that

information once it’s there. And because not all applications are

built to suitable enterprise standards for security, they can also

serve malicious links and act as entryways for attackers to infiltrate

a company’s network—giving them access to your systems and data. All

of these issues can lead to regulatory compliance violations,

sensitive data exposure, IP theft, operational disruption and

financial losses. While these apps provide enormous productivity

potential, there are serious risks and potential consequences

associated with their adoption if not done securely.

Take for example:

• Marketing teams using an unsanctioned application that uses AI to

generate amazing image and video content. What happens if the team

loads sensitive information into the app and the details of your

confidential product launch leak? Not the kind of “viral” you were

looking for.

• Project managers using AI-powered note-taking apps to transcribe

meetings and provide useful summaries. But what happens when the notes

captured include a confidential discussion about this quarter’s

financial results ahead of the earnings announcement?

• Developers using copilots and code optimization services to build

products faster. But what if optimized code returned from a

compromised application includes malicious scripts?

These are just a few of the ways that well-intentioned use of GenAI

results in an unintentional increase in risk. But blocking these

technologies may limit your organization’s ability to gain a

competitive edge, so that isn’t the answer either. Companies can—and

should—take the time to consider how they can empower their employees

to use these applications securely. Here are a few considerations:

Visibility: You can’t protect what you don’t know about. One of the

biggest challenges IT teams face with unsanctioned apps is that it’s

difficult to respond to security incidents promptly, increasing the

potential for security breaches. Every enterprise must monitor the use

of third-party GenAI apps and understand the specific risks associated

with each tool. Building on the understanding of which tools are being

used, IT teams need visibility into what data is flowing in and out of

corporate systems. This visibility will also help detect a security

breach so it can be identified and rectified quickly.

Control: IT teams need the ability to make an informed decision on

whether to block, allow or limit access to third-party GenAI apps, on

either a per-application basis or leveraging risk-based or categorical

controls. For example, you might want to block all access to code

optimization tools for all employees but allow developers to access

the third-party optimization tool that your information security team

has assessed and sanctioned for internal use.

Data Security: Are your teams sharing sensitive data with the apps? IT

teams need to block sensitive data from leaking to protect your data

against misuse and theft. This is especially important if your company

is regulated or subject to data sovereignty laws. In practice, this

means monitoring the data being sent to GenAI apps, and then

leveraging technical controls to ensure that sensitive or protected

data, such as personally identifiable information or intellectual

property, isn’t sent to these applications.

Threat prevention: The potential for exploits and vulnerabilities can

be lurking underneath the surface of the GenAI tools being used by

your teams. Given the incredibly fast rate at which many of these

tools have been developed and brought to market, you often don’t know

whether the model being used was built with corrupt models, trained on

incorrect or malicious data, or is subject to a broad range of

AI-specific vulnerabilities. It is a recommended best practice to

monitor and control data flowing from the applications to your

organization for malicious or suspicious activity.