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Leveraging AI to Optimize Networks and Secure Data – Thought Leaders

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by David Ryan, Vice President of Sales, Cynamics

Massive amounts of data are being generated daily; by some accounts, a staggering 2.5 quintillion bytes of data are being created every day. In today’s world, proprietary data is one of the most valuable assets organizations have, it underpins the operations, identifying ways to improve processes and increase efficiencies, it provides insights into customer information, purchasing behavior and contains supply chain information. An organization’s data is one of the most crucial elements of its business, and it must be protected.

Unfortunately, humans alone can’t possibly manage the volumes of data on their own. Aside from that, and more importantly and frightening is how much of an organization’s data is left unmonitored, with the majority of networks unprotected. Not only could this result in a breach that could devastate your organization, but it could also leave you in legal jeopardy as you have compromised your trust and relationship amongst business partners, customers and suppliers. Protecting your network and ultimately your proprietary data should be a top priority for all organizations.

Legacy data approaches are failing

When companies continue to use legacy cybersecurity approaches, they risk the inherent blind spots and backdoors that leave them exposed to vulnerabilities and at an increased risk for being attacked. The networks themselves have dramatically changed – modern networks grow more complex and interconnected every day. To stay competitive, businesses have upgraded their systems and equipment, garnering them with sensors and connectivity to streamline operations and provide quicker time to value. This phenomenon, also commonly referred to as the Internet of Things, has sparked a stark increase in the interconnectedness of organizations. With the onslaught of these new advancements and equipment all containing sensors to connect to the network, comes the inherent potential increase for vulnerabilities. Malicious actors are always looking for ways to infiltrate sensitive networks, and overly complex, linked systems allow them to sneak inside without detection.

Most legacy approaches fall short in adequately monitoring modern day network traffic.  Typically deploying rule-based or signature-based detection products that are programmed to look for specific threat details. These tools are essentially being told what to hunt for – which is a major problem when you consider the ever-evolving nature of cyber-attacks. If the system isn’t taught to look for something, it won’t find it – and bad actors are opportunistic and constantly looking for new ways to infiltrate networks unnoticed.

Using AI to tackle the data deluge

The right application of AI technology can provide complete visibility of networks.  However, there are a few factors holding back more widespread adoption. For one thing, many security teams are small and strapped for resources (especially given the ongoing and off-cited cybersecurity skills gap) and they lack the ability to properly research and investigate available solutions. Additionally, there’s a lot of uncertainty and doubt related to AI-based solutions, in large part because vendors claim to be using automation and AI, but they are not.

For a product or solution to be truly AI-based, it shouldn’t require significant manual setup or control. You should be able to add it to your network environment and it immediately runs autonomously with little-to-no manual interference. A true AI-based solution automatically monitors your network to detect anomalies and threats, which triggers appropriate policies autonomously and intelligently. It oversees everything taking place on your network, so you can focus on operations.

AI in Network Detection and Response (NDR) solutions can discover hidden traffic patterns, sequences and behaviors that precede attacks for rapid, precise prediction. This helps to block the most damaging threats occurring today, including DDoS attacks and Ransomware, compromised endpoints and C&C communications, long before they access sensitive assets.

While it’s impossible to sort through massive amounts of data and network packets manually, even with automated solutions, it isn’t practical or efficient. Fortunately, through sample-based protocols that leverage AI, organizations now should expect full network visibility from any NDR solution. These methods use advanced sampling techniques to minimize the resource demand on the network, made possible by sophisticated AI and ML.

Best practices for using AI

To begin integrating AI into your network security, investigate products that won’t increase your attack surface – solutions that are vendor-agnostic and thus won’t require infrastructure changes. Solutions that are cloud-native and don’t require hardware appliances, agents, probes or sensors to your network.  Are solutions that require a lot of manual effort really delivering artificial intelligence? Or are they overburdening your already overwhelmed, understaffed and underprepared security teams?

Determining which AI-driven NDR is right for your business requires considering two main factors. First, identify what access or changes need to be made to your network for the solution to work properly. Secondly, determine if the solution requires appliances, agents, probes, sensors etc. to work effectively.

Ideally, solutions that are deployed in the cloud and require zero on-prem presence are preferred. These solutions generally scale faster, provide quicker time to value, are more resilient, portable and cost effective. Additionally, as you introduce new devices into your infrastructure, you start to increase the attack surface, network complexity and the potential for vulnerabilities to slip through the cracks.

True artificial intelligence should be artificial, it should be able to learn and build out your network without requiring upfront manual effort, time and resources. The threat landscape is continuously changing, staying ahead of the evolving environment is crucial for threat detection and prediction, as well as remaining successful in preventing cyberattacks.

Network security enables business

Humans can only do so much, and with the vast increase in interconnectivity, data generation and network traffic, it’s improbable and irresponsible for organizations to assume security teams can handle it without the use of AI.  The right application of AI technology can provide complete visibility and uncover sophisticated, unseen threats by analyzing just radically small traffic samples without any agents or appliances. This means network performance doesn’t suffer as security increases. All your data is secured, allowing you to glean new business insights, improve processes and uncover new market opportunities, turning network security into a business enabler and not an ongoing cost center.

David Ryan is vice president of sales at Cynamics. David is a sales executive with 10+ years of experience in the B2B space, specializing in cybersecurity. He is passionate about building relationships with customers and arming them with the latest information regarding the ever-changing cyber threat landscape. At Cynamics, David is responsible for running and leading high-performance sales teams that drive revenue growth and deliver great customer experience and unparalleled support. David is focused on helping customers secure their networks, allowing them to detect and predict threats before they occur.