Endpoint security is often a struggle for businesses to effectively maintain. A significant amount of data is stored on a large number of mobile devices, making security difficult to manage. In addition to this, endpoints and cybercrime have evolved in such a way that traditional network security is needed, but not sufficient on its own. Organizations need a supplemental service to assist with endpoint security. Companies have begun using AI and machine learning in conjunction with their mobile security strategies. Why is this approach worth considering?
Cybercrime has shifted in focus and methodology in recent years, which means that cybersecurity must shift as well in order to adapt. Today’s cybercriminals are more interested in stealing valuable intellectual property and data, rather than a company’s money. Additionally, data weaponization is increasing. This means that cybercriminals are leveraging stolen information as a means to implement social engineering schemes and blackmail business executives. Not only that, but Internet of Things devices, such as voice assistants, are also regularly targeted now. The environment has spread and become more interconnected, meaning that traditional approaches to network security are insufficient.
At the moment, cybersecurity is very malware focused. Businesses are intent on defending against malware infections, and for good reason. Malware does not show any signs of falling out of use, as criminals still use it to gain access to the data they want. Though antivirus software, firewalls, and other perimeter defenses are still necessary, they can’t completely protect endpoints on their own. This is because the traditional perimeter approach doesn’t acknowledge that data is located on the endpoints. With the huge number of mobile devices that need protection, it’s virtually impossible for many organizations to maintain security solely on manpower.
Endpoint security can be augmented through the use of machine learning, which can handle the complex algorithms needed to protect data in this age of extreme connection. Machine learning can be used for facial recognition tools as well as procedures that ensure that security tools are used in the correct way and in the right scenario. Through these capabilities, communication about possible risks and situational awareness are promoted. While human analysts can be the second line of defense by verifying situations and putting plans into action, machine learning performs data protection and prevents intrusions by other machines. Perhaps most importantly, machine learning can handle protecting the data located on a significant number of endpoints.
Machine learning can offer notable assistance in the field of cybersecurity by taking some of the heat off of overworked analysts, giving them the opportunity to perform other tasks instead of focusing only on endpoint security. Today, traditional network security methods aren’t going to cut it. To improve your company’s endpoint security and protect its data, consider implementing machine learning.
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