How AI-Driven Network Detection and Response Closes Security Gaps
This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, Cynamics Co-Founder and CEO Eyal Elyashiv offers four ways that AI-driven network detection and response can help close your security gaps.
Technological changes are evolving at an unprecedented rate – so why aren’t most network detection and response (NDR) solutions? NDR and network performance monitoring and diagnostic tools created 30 years ago can’t keep up with today’s complex, interconnected networks. With each new connection comes a possible vulnerability and a higher risk profile. Cybercriminals are always looking for corporate networks that are complex and filled with security gaps for them to sneak through unnoticed. This security challenge has persisted despite the many efforts of enterprise IT security teams.
The problem is one of pace; neither legacy solutions nor human analysts are able to keep up.
Enterprises need assistance from AI-based solutions to enable full visibility into their network. network detection and response (NDR) solutions derive particular benefit from AI. However, to implement NDR well, organizations need clarity on its key elements, both before and after implementation.
The need for AI assistance
With the increased complexity of networks and the increased volume of data, the reality is that human analysts are incapable of monitoring all of it, alone. To make matters worse, the industry is experiencing an estimated shortage of 2.72 million skilled cybersecurity professionals – there just aren’t enough skilled people to adequately defend organizations’ critical assets. Instead, the industry must learn how to use tools like AI and ML to supplement these skills gaps. The lack of capable and experienced cybersecurity talent can leave networks vulnerable to a myriad of threats.
AI Increases Network Visibility
At the same time, network defenders find themselves fighting the battle of smart networks, which boast not only complexity but increased scale. Anomalies, attacks, and threats can start with one simple click and begin at one of the hundreds or thousands of devices connected to the network – workstations, routers, switches, and more, significantly compromising network security.
Adding specific network monitoring and detection solutions to each endpoint is expensive and impractical, and it can negatively impact network performance. Monitoring each network component separately is insufficient; detecting a sophisticated attack requires a holistic view of the network and a comprehensive analysis of network patterns across devices.
Artificial intelligence and machine learning give teams the comprehensive view they need. With sample-based techniques, the most likely behavior of all network traffic is extrapolated based on radically small traffic samples from every network device, including private or public cloud and legacy routers. Then, with the use of AI/ ML, it autonomously learns the most important network fields, using these to summarize the network state in each device at every timestamp continuously. It can understand changing network patterns or trends and any suspicious behavior autonomously.
The benefit of AI detection models is that they never stop analyzing network traffic patterns. They do this over time and in several layers – including each device by itself, the entire network level, and groups of devices, looking for suspicious behaviors. These models, which are based on analyzing samples of network traffic, greatly reduce the processing time, compared to current solutions that must collect, process, and analyze each and every packet. Such models enable early and faster detection, and require minimum resources.
Detection of this type can reveal traffic patterns in real-time that hadn’t been detected previously, without the expensive and impractical need to monitor every packet. This makes AI-based NDR solutions time-efficient, cost-effective and holistic in their network coverage.
AI-based NDR can predict threats and hidden patterns autonomously – and before attacks happen. It automatically monitors the network to detect threats and anomalies for rapid, precise prediction, while you focus on operations. This triggers appropriate policies to uncover today’s most damaging threats, including ransomware and DDoS attacks, long before they reach your sensitive assets.
A solution of this kind boasts a major advantage: you don’t have to make any changes to your network. Some are even agnostic to network hardware and architectures. You don’t need to install any appliances or agents, and it’s non-intrusive, which reduces risk.
A Critical Role in Network Security
It’s unrealistic to think that solutions designed three decades ago can protect today’s complex networks against sophisticated attackers. Thirty years in the technology realm is like a lifetime due to the rapid pace of change. Another massive technology change that has shaken up the industry is the advent of AI- and ML-based security applications, including NDR.
AI-driven NDR offers complete network visibility across all endpoints; some solutions accomplish this while using only a fraction of network traffic. Threats are quickly and accurately detected, and network vulnerabilities and deficiencies are immediately identified. The fact that modern NDR costs less and is non-intrusive is an added bonus. For today’s complex, interconnected networks, next-gen NDR has a critical role to play.
- How AI-Driven Network Detection and Response Closes Security Gaps - February 4, 2022