Protecting an organization's digital assets is more important today than ever before. In fact, digital security has had to keep up with data thieves who consistently find new and creative ways to breach a system that you might think is secure.
In the past, businesses and individuals could protect themselves from a breach using just a few security technologies, along with practices that focus on the internal network. However, with increasing threats from cybercriminals, businesses need to move away from past strategies to implement a threat prevention culture that focuses on both the detection and response of a
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To resolve this issue, many organizations are using endpoint security solutions with the most powerful capabilities, such as machine learning and artificial intelligence (AI).
The Evolving Definition of Endpoints
Because networks have continued to evolve, organizations need to redefine how they view an endpoint. According to Sri Sundaralingam of Symantec's, in the past, network endpoints were defined or identified as any device that communicates across a network within the corporate firewall. These can include printers, PCs, modems, and routers.
When the cloud was invented, this allowed for organizations and businesses to expand their network to include devices outside the firewall. As a result, IT communities began to rethink what constitutes an endpoint. Sundaralingam goes on to say that in our modern world, an endpoint includes any device that can access a corporate network, including smartphones, tablets, PCs, wearables, Internet of Things, and more. These devices that connect to a corporate network can also include endpoints such as vending machines.
Typically, these devices have less protection when it comes to a
Many businesses who haven't had a breach, understand that a
Since cyber threats are becoming more sophisticated, EDR tools are put into place to address the need for continually monitoring and reacting to
How Machine Learning and AI Identify Cyber Threats
The most serious of cyberattacks actually go beyond the endpoint. In fact, the endpoint is really the location cybercriminals use to expand a more aggressive attack that involves using small steps over days,
In the CDW Cybersecurity Insight Report, 39 percent of respondents considered using next-generation endpoint technologies that combine AI, machine learning and behavioral analysis to circumvent aggressive and sophisticated cyber attacks. The point of AI and machine learning technology in this context is to identify the new, ever-changing malware attacks. In fact, a business should have multiple ways to detect an attempted breach and prevent attacks, such as commodity malware and
When using advanced machine learning that employs multilayered threat assessment, organizations are better able to identify how static files interact with other files, URLs, or machines. Machine learning can also analyze enormous amounts of data to identify if a code is likely to be malicious. Put another way, advanced machine learning, according to Sundaralingam, is the first responder when a cyber attack occurs. From here, the machine is able to detect a malware presence and then stop it. When an organization uses machine learning and behavioral analysis, it is better able to identify attacks, especially large-scale attacks.
To keep up with the constant threat of
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