Defining Next-Gen Cybersecurity

To keep up with the latest cyberattacks, you need to understand what next-gen cybersecurity is all about. There’s a lot of buzz around AI, Machine learning, Endpoint protection, and SIEM, but can you protect your business? What should you focus on? Here are some critical aspects of next-gen cybersecurity. 

AI

When it comes to preventing cybercrime, AI is the key. With the rise of digital channels and the emergence of malware, organizations face exponentially greater attack surfaces. These attack surfaces include 5G networks, which could expose an enterprise outside its firewalls and into the private networks of employees and partners. However, the use of AI is not just a tool for preventing cybercrime; it can also help businesses detect new threats that were previously undetectable.

With its ability to analyze vast amounts of data, AI is revolutionizing next-generation endpoint security . AI is a force multiplier than human cyber professionals, helping organizations detect attacks, identify vulnerabilities, and automate decision-making during threat hunts. In addition to meeting today’s increased security needs, AI is also expanding the capabilities of government environments. While AI cannot replace human experts in cybersecurity, it is an indispensable tool for enhancing security operations.

Machine learning

Machine learning has been touted as a critical factor in the fight against cybercrime. However, it has its limitations. Machine learning algorithms can only recognize specific types of data, not all. For example, machine learning algorithms can’t learn to recognize a laptop with different styling; they need many different models to admit it. In addition, machine learning solutions are not as smart as humans in data environments with access to large amounts of data.

Cybersecurity

The rapid evolution of digital data has made reactive approaches to cybersecurity ineffective. Advanced malware is being developed and distributed at unprecedented rates. Additionally, employees are accessing corporate data from more devices than ever. As a result, security solutions must pivot to a more proactive approach that leverages machine learning to protect these organizations. The application of machine learning in cybersecurity will allow organizations to secure corporate data across all cloud applications, identify patterns, and detect high-risk data outflows.

Endpoint protection

Next-Gen cybersecurity is not only about protecting your endpoints but also detecting threats off-network. A comprehensive endpoint protection solution eliminates potential blind spots and gives you real-time threat intelligence. The data generated by Next-Gen endpoint protection can be presented explicitly and graphically that’s easy to understand for everyone. It doesn’t require security experts to operate. Most endpoint protection solutions are built for the average user and are simple to implement.

Earlier, next-gen endpoint security solutions were defined as those that did not use traditional file scanning. Today, cybersecurity solutions employ automated threat detection and real-time predictive methods to identify and mitigate threats. This enables endpoint protection to be automated and flexible enough to adapt to various network environments. However, it is essential to note that these solutions are not necessarily able to detect all attacks. Some of them can even detect phishing attacks, which is why it is vital to use an endpoint protection solution that does both.

SIEM

Using SIEM is a critical component of defining Next-Gen Cybersecurity. It is a centralized security monitoring platform that spans cloud-based technologies and integrates log management with behavior analytics-based advanced threat detection. Its capabilities extend beyond traditional security monitoring, eliminating security blind spots such as limiting SIEM’s capabilities to on-premises data. The key to defining Next-Gen Cybersecurity is identifying a company’s exposure to insider threats – employees, contractors, business associates, and competitors. Insider threats do not have a perimeter so that they can compromise a company’s infrastructure.

Today’s SIEM solutions can read logs from various security monitors and detection tools. In the future, SIEMs can consolidate the outputs of these systems, reducing false alarm rates and improving detection accuracy. However, if the SIEM solution is not up to the task of defining Next-Gen Cybersecurity, it will be difficult to measure its value.

Data loss prevention

The term “data loss prevention” is often misused to describe data protection during a security breach. In truth, data loss prevention is a critical component of cybersecurity programs. This practice prevents the exposure of sensitive information, such as financial and client data, and regulated and personal information. Companies are increasingly motivated to implement such a strategy to prevent the exploitation of their intellectual property and proprietary data. Losing such an advantage could have significant financial ramifications.

While a DLP solution can prevent such incidents, it may not be enough to protect sensitive data from external threats. In addition to preventing external threats, data loss prevention can protect confidential data at rest, in use, and motion. For example, data is less secure in transit because it moves over the internet, between networks, or through private networks. Adequate data protection for these instances is essential to protect confidential information.