Ransomware attacks are on the rise, and endpoint solutions are fighting back to protect your data. As cybercriminals get more creative with ransomwares to attack, there are steps that can be taken by IT professionals to prevent this malware from reaching their network.

With the rise of cybercrime, endpoint protection has become a top priority among all industries and government agencies. It’s not surprising that organizations are investing heavily in these solutions to protect their data.

Endpoint security is the last line of defense in your organization’s network defenses. To help maintain control of your endpoint, it’s important to know the latest methods for keeping your data safe. This article will provide an overview of endpoint security evolution and how endpoint security solutions are working to protect your data in 2022.

Zero Trust protocols

The Zero Trust model has been on the lips of most security experts in recent times, and for good reason. In the Zero Trust model, your endpoints are protected by constant authentication and verification of users and devices from all other users, applications and services on your network.

Zero Trust works because all users and devices are untrusted and must be authenticated and validated before being granted access. It seems a bit extreme, but drastic times call for drastic measures.

In the 2021 Gartner Magic Quadrant EDR report, Gartner made it clear that zero trust protocols are now the standard in the endpoint protection market.

Self-healing endpoint devices

The ability for endpoints to repair themselves seems like a dream come true, but it’s becoming a reality. There are three fundamentals of self-healing endpoints:

  • The ability to confront external threats.
  • Mechanisms to address software decay.
  • The ability to self-heal firmware.

Self-healing endpoint solutions must be built to purpose, and this will require complex strategies and cooperation between vendors and device manufacturers. However, this is not impossible and will most likely lead to increased uptake of self-healing solutions in the marketplace.

Addressing software decay is a critical factor because it goes hand-in-hand with an endpoint’s ability to self-heal at the firmware level. As endpoint security is software just like any other, it is also prone to the same hazards of software decay.

By being able to self-heal at the firmware level, endpoint security solutions are able to plug vulnerabilities within the core and continually monitor for any firmware-related vulnerabilities.

Tighter scrutiny of behavioral analytics

Behavioral analytics in the context of endpoint security comes down to gathering information from endpoints and correlating it to potential threats that could be exploited.

Let’s say, for example, there’s a small amount of malware that has been specifically targeting and attacking the user’s browser. However, with the collection and analysis of browser sessions, behavioral analytics tools can link browser sessions back to the applications that the browser is running in the background. This allows organizations to identify and eliminate malware infections that have been residing on endpoints for extended periods of time.

Endpoint behavioral data is receiving extensive scrutiny so that malicious behavior patterns can be more accurately identified. To ensure that all data is analyzed in the proper context, advanced machine learning algorithms are being integrated into endpoint solutions to better understand what is being collected, from whom it’s being collected, and what it is being collected for.

The collaboration of ML and AI in endpoint security

Artificial intelligence and machine learning are becoming ubiquitous in endpoint protection. The inclusion of both AI and ML in endpoint security platforms has played an essential role in helping organizations in finding, stopping, and addressing issues faster than ever before. In some cases, machine learning can enable organizations to discover new threats and information before they’re even aware that there’s a potential issue.

In the coming years, with new technologies like neural networks and deep learning, machine learning will become increasingly important to endpoint security solutions.

Endpoint security, with its flexibility and wealth of functionality, can no longer be thought of as a discrete and standalone product. Instead, it needs to be considered as an integrated part of an overall cybersecurity solution.