Network Security Monitoring
January 12, 2022
Most business leaders today are aware of artificial intelligence’s (AI) role in process automation, which has been increasingly sought-after by organizations all over the world. Its role in cybersecurity is more important than ever.
AI and machine learning have applications beyond workflow automation and one area where it’s currently being used to great effect is in the cybersecurity field.
The cybersecurity landscape for organizations has evolved significantly in just the last few years, with cyberattacks more widespread and sophisticated than they have ever been.
Cybersecurity professionals and software providers have had to up their game in response, employing tech like AI to combat modern threats.
Read on to learn more about how AI can be used to secure your business now and manage the risk of a breach in future.
Businesses can no longer afford to rely on old cybersecurity systems or “set it and forget it” antivirus software—many legacy applications still in use by businesses today lack the capabilities necessary to avoid being breached.
Modern cybercriminals use means to circumvent legacy cybersecurity solutions with ease, often slipping under the radar and breaching an unknowing victim.
A 2020 report indicates that organizations take an average of six months to detect a data breach in their network.
It’s important for businesses to understand and leverage the new technologies available to them in order to stop modern attacks that threaten their data, networks, and business as a whole.
When we talk about leveraging new technologies, this would be in the context of building a complete security tech stack that incorporates automation, next-gen antivirus, password management tools, and artificial intelligence (among others) to create a layered defense.
AI and machine learning are now an integral aspect of modern security and come with several benefits that standard legacy systems are not able to match.
AI uses its intelligence to continuously learn and adapt to improve network security over time—this is referred to as machine learning.
It monitors network activity and behavior to recognize patterns, store them, and use that information to help identify unusual behavior that could be the result of a cyberattack or the presence of a hacker.
It also learns from past attacks or attack attempts by storing that data and looking for similar patterns rising again.
This consistent adaptation gives a business’ network security a leg up on hackers by establishing an environment in which the machine learning AI understands what network activity should look like, and then use that knowledge to spot unusual behavior—such as a large amount of data being transferred out of the network.
Additionally, AI can handle a lot more data than a human team and at a much quicker rate.
Companies with a lot of network traffic have a lot of data to sort through and analyze to find potential threats—AI is necessary for the purposes of data analysis because it can perform the same job a human can in a fraction of the time.
Detecting threats is the first step toward repelling an attack, the next is responding as quickly as possible to limit damage and downtime.
AI can analyze extremely large data sets faster and respond to any irregularities instantly, quarantining threats and flagging them to the appropriate stakeholder to review (typically internal IT or a managed service provider (MSP) vCISO).
AI can identify threats that humans cannot.
With a new cyberattack happening every 39 seconds, hackers are constantly throwing unique, automated attacks at businesses and continue to successfully thwart them.
For many cybercriminals, it’s more advantageous for them to gain access and remain undetected, as they are then able to snoop around as they please, completely unbeknownst to the victim business.
Consider the attack on Panasonic in 2021, which went undiscovered for five months. This is a common occurrence in organizations today and a key reason for having AI capable of detecting behavioral anomalies in a cybersecurity plan today.
Learning from past attacks, pattern analysis, and advanced threat detection allow AI to see even the smallest hints of malicious behavior within a network so it can act fast to stop them.
Unknown threats are a common occurrence for businesses. 300,000 new pieces of malware are created daily.
As we’ve mentioned above, AI is excellent at identifying use patterns and discerning if the correctly authorized people are accessing and handling data.
This is evident in AI-based authentication and access controls which give businesses another layer of protection by limiting who can access what and by having a machine monitor it all 24/7.
The integration of AI into authentication protocols typically manifests in studying the login behavior of a user—the most common example of this would be flagging when a user is accessing a network from an unexpected location; like a coffee shop instead of the office.
More devices are used by businesses than ever before and securing all those endpoints has become a challenge, especially when they’re used remotely and on unsecured, public Wi-Fi networks.
AI helps protect endpoints through behavioral monitoring and analysis so it can identify out-of-the-ordinary device use.
With hundreds or even thousands of devices in use by a single business being commonplace, it’s necessary to enlist the help of AI to monitor these devices 24/7 and with a quick response time to stop attacks as fast as possible.
Using AI in your cybersecurity strategy is simply a must for modern businesses who are consistently subjected to new and unique threats that require the latest technology to stop.
AI can more effectively secure your endpoints, increase response times to potential attacks, and use machine learning to predict attack vectors, find vulnerabilities, and sniff out malicious actors.
Learn more about how your business can implement AI-based cybersecurity by contacting a DOT Security expert today.