How Does Network Security Handle AI?How Does Network Security Handle AI?

AI is reshaping how companies do business. The technology offers tremendous promise -- along with potential pitfalls -- and many businesses are investing in the technology. But are the systems designed to protect corporate assets up to the task of securing AI?

The answer is likely no.

In fact, network managers must secure their networks, and they must understand how AI network security differs from standard security. More specifically, what makes AI network security different from standard security? How do you prepare for it?

These are key questions facing network managers as AI implementation skyrockets. According to a Statista survey on the global AI market, the market is projected to record a compound annual growth rate of 26.6% over the next five years, eclipsing the $1 trillion mark by 2031.

Clearly, companies are rushing to adopt AI. But even as the technology becomes more essential, managers have to weigh the network security repercussions AI is likely to bring.

Existing Systems Can Help, to a Point

There is some good news: Today's network security practices are mature disciplines. They successfully monitor a vast number of potential threats -- from malware to social engineering --and can even predict where attacks are likely to occur, thanks to advances in machine learning and AI.

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Network security relies on tried and proven technologies, among them:

Yet these traditional security tools might not be sufficient to monitor AI threats. Bad actors are looking for ways to penetrate the AI resource itself. This means enterprises must find different ways to combat these threats.

AI prompts and chats

These are attackers' favorite methods to crack into AI systems. For example, in December 2023, a user manipulated the response of a Chevy dealer's chat function to trick the company into selling a $76,000 vehicle for $1. In May of the same year, Samsung employees using ChatGPT for a review of internal code and documents accidentally leaked confidential information, leading Samsung to ban the use of generative AI. Yet, banning the use of GenAI when it is becoming a dominant tool in industry isn't the answer.

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Data poisoning, model bias and inaccuracy

The intent with this method is to gain access to AI code or data and modify it so it turns out inaccurate results and recommendations that can mislead company decision-makers into faulty actions and outcomes.

Detecting when AI models begin to vary and yield unusual results is the province of AI specialists, users and possibly the IT applications staff. But the network group still has a role in uncovering unexpected behavior. That role includes:

Larger organizations with highly sensitive AI and intellectual property might hire network forensic specialists who can dig deep to discover the root of an AI invasion if and when it occurs -- to seal off the vulnerability and harden security so that such a compromise can never happen again.

Deepfakes

In 2024, the volume of fake video calls, text messages, emails and voice calls more than tripled the number reported a year earlier. In 2025, these attacks grew by another 19% according to Surfshark.

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Deepfakes are used to commit fraud, manipulate political content and fake the identities of corporate decision-makers to mislead companies. Network staff members play key roles in preventing these exploitations by doing the following:

Employees are often the target of deepfake perpetrators. Managers must advocate for and, if possible, participate in education and training. Some telltale signs of deepfakes include lip movements and words spoken out of sync, unnatural voice intonation or eyes that never blink. Such deepfakes can be tracked and traced back to their sources.

How to Implement Network Security for AI

So what can network professionals and AI developers do? Consider these three important steps.

1. Develop an effective strategy to combat AI prompt injections

To do this, both application and network teams need to ensure strict QA principles across the entire project -- much like network vulnerability testing. Develop as many adversarial prompt tests coming from as many different directions and perspectives as you can. Then try to break the AI system in the same way a perpetrator would. Patch up any holes you find in the process.

2. Enforce least privilege

Apply least privilege access to any AI resource on the network and continually monitor network traffic. This philosophy should also apply to those on the AI application side. Constrict the AI model being used to the specific use cases for which it was intended. In this way, the AI resource rejects any prompts not directly related to its purpose.

3. Red teaming

Red teaming is ethical hacking. In other words, deploy a team whose goal is to probe and exploit the network in any way it can. The aim is to uncover any network or AI vulnerability before a bad actor does the same.

This approach lets companies proactively plug any network security holes. Often, network managers outsource this work to a professional firm, which conducts the simulated network attacks. Red teaming is a valuable testing process that every network manager should consider, especially given the many unknowns of AI.