Simplifying Network Security: How AI is Changing the Game 

As companies come to rely on digital systems in everything they do, network security has become more important than ever. Unfortunately, with that digital transformation comes complex networks to support it, and thus complex network security.  

Gone are the days when a company could rely on firewalls at the perimeter to protect them. Today, users can be anywhere from a corporate office, to working from home, to the coffee shop across the street or even in another country. And applications are rarely served strictly from the company datacenter. They are instead being sourced by some combination of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or Software as a Service (SaaS), residing in one or multiple cloud services such as AWS (Amazon Web Services), Microsoft Azure, or GCP (Google Cloud Platform). IT staff are overworked and struggle to find the time needed to configure security for these varying systems and connectivity paths. 

In this article, we will explore how AI (artificial intelligence) is helping to simplify the management of these complex network security systems, how AI is improving data security, and why AI is a game changer for the future of network security.  

How Does AI Simplify Network Security Management? 

In general, AI can be used to automate security tasks, detect threats, and respond to attacks. AI is particularly good at analyzing large amounts of data and identifying when something looks suspect or bad that a human may not have seen or simply did not have the time needed to do proper analysis. AI-powered security tools can detect these patterns, anomalies, and abnormal behaviors, and bring them to the attention of security teams, allowing them to respond quickly before any damage is done. With AI-based security tools, this can be done without having to spend time configuring the system on exactly what it is looking for. This allows the security team to focus on actual security issues, not constantly updating configurations or chasing false-positives.  

Take email, for example. One of the AI-powered technologies Teneo is partnered with, Abnormal Security, uses behavioral analytics to analyze tens of thousands of signals to determine whether an email actually came from the person in the “From” field. It looks at identity, content, and even tone to learn the types of emails that are usually exchanged between email users and can stop a Business Email Compromise (BEC) attack in its tracks.  That means that if the CEO’s account is somehow actually compromised by a bad actor, Abnormal’s behavioral AI will know that it is not the CEO asking finance to change the bank information for a supplier and put a rush on that payment.  It also ensures that vendor and other external communication are valid and safe.  

The key here is, not only is it effective, but it does not require complex configurations to make that happen. It does not need rules or policies, as it learns what it needs as it goes. It can be deployed, learning its baseline in 5 minutes and identifying BEC attacks in a few days. Now that is simplified network security management! 

AI Improves Data Security 

We already mentioned that AI is good at seeing patterns in large amounts of data, and how that improves the detection of email phishing and BEC attacks. But can it look at a single file and figure out if that file is malware? The answer is yes, and the next example underscores how AI is improving data security in ways not possible using non-AI technology. 

Teneo’s partner Deep Instinct has been training a neural network for years on what malware looks like. When an AI is trained on a large amount of data, it is called Deep Learning (DI). DI is the most advanced form of AI Machine Learning (ML). Deep Instinct has one of only six major deep learning frameworks in the world—and the only one purpose-built for cybersecurity.  

When a neural network is trained, it develops hidden layers between the input and the output layers where it learns, on its own, what to look for to decide if a file is good or bad. The indicators it finds and recognizes may never have been chosen by an analyst or are simply too complex and nuanced. Over time, those hidden layers are correct over 99% of the time with less than .01% false-positives. Deep Instinct compiles those hidden layers into a client which continues to perform at that high accuracy level, even when offline. As a demo, they will take a 6-month-old client and run it against 100 of the newest viruses, freshly downloaded from a site like VirusTotal. Now that is confidence! And that is how AI is improving security beyond what even the top researchers and analysists could create.  

Hackers are now using weaponized AI in their attacks. Traditional security tools were never designed to cope with the dynamic nature of an AI-based attack. Large language models like ChatGPT can be asked to create an email that will circumvent traditional email filters, and it will dutifully go about carrying that out. It takes AI to defend against AI. Thankfully, solutions like Abnormal Security, Deep Instinct, and other technologies chosen by Teneo are doing just that. 

AI is Changing the Game 

As we have discussed, AI improves network security in critical ways, including: 

• Faster Threat Detection: AI-powered security tools can detect and stop potential threats in real-time. This helps security teams to respond quickly and prevent any damage from being done.
• Better Anomaly Detection: AI can analyze network and email traffic to identify patterns of behavior that are abnormal or suspicious, empowering security teams to quickly find potential threats.
• Simplified Configuration: AI often excels because of its ability to learn, whether that’s ML, DI, or behavioral analytics.  This means less or no manual configuration, thus simplifying deployment, day-to-day operations, and long-term effectiveness.
• Incident Response: AI can deliver lower false-positives and thus help security teams respond to security incidents more quickly and efficiently by automating tasks such as blocking a BEC attack (Abnormal), or confidently quarantining a file believed to be malware (Deep Instinct).
• AI Attack Prevention: Hackers are now using weaponized AI in their attacks. Traditional security tools were never designed to cope with the dynamic nature of an AI-based attack. AI-based security tools future-proof security solutions against new and ever-changing AI-based threats.

The importance of AI in network security cannot be overstated. Cybersecurity threats are becoming increasingly sophisticated and complex, making it difficult for traditional security tools to keep up. AI-powered security tools can analyze large amounts of data quickly and accurately, identifying threats missed by traditional security tools. Additionally, AI-powered security tools can automate many of the routine tasks involved in network security, freeing up time for security teams to focus on more critical tasks. Organizations leveraging AI will stay ahead of the curve and protect their networks from future potential attacks. AI in network security is not just today’s buzzword or marketing trend, it is, indeed, the future of network security. 

For more information about Teneo’s Deep Learning & AI-based Security solution, visit our solution webpage or book a 30-minute introductory session to explore how Teneo can help with your approach to cyber security using AI.  

Author:  Steve Evans, SVP, Technical Customer Success