Using AI Cybersecurity Solutions to Stop Ransomware

Growing interconnectedness makes it harder to detect ransomware before it causes harm. Whether data is processed locally or in the cloud, the risk of a breach is the same. But edge computing also makes it easier for hackers to break in because sensitive data is stored and processed on a wider range of systems. As the footprint gets too big, it gets harder to protect large-scale environments where computers are everywhere, and it may even become impossible. The fast proliferation of IoT devices and the expansion of edge networks are changing how chief information security officers (CISOs) and IT security professionals strategize for and secure their edge and IoT infrastructures.

Ransomware in Edge and IoT

Edge security protects users and apps at the edge layer of a company’s network, where sensitive data is very vulnerable to security threats. Edge devices have a lot at stake, so CISOs and other business leaders need to make sure edge environments are safe. This includes encrypting data while it is at rest and in transit, monitoring the network and keeping control through a centralized management dashboard that controls how devices interact with the computing environment.

A distributed network is one way edge computing makes an environment less safe. If there are more devices in more places, they are more likely to interfere with each other or cause other problems. Physical threats could include tampering with devices to put malware on them or making mistakes that damage the device or data. To keep physical security risks like ransomware from affecting edge devices, the company’s premises should be set up to detect threats in real-time.

Because there are more edge devices that can store and process data, there are also more virtual security risks. Hackers could steal data, stop operations, get into corporate systems and run ransomware by connecting to these devices from afar. In short, an attacker can use one device to break into the whole organization’s network.

Cybercriminals are finding new ways to get around technical security measures, use edge/IoT vulnerabilities as weapons and improve the sophistication of their attacks. Ransomware attacks can happen to any company or industry.

AI Strategies for CISOs to Advance Edge Security

To protect against ransomware, malware and DDoS attacks effectively, CISOs and security professionals need to use a variety of methods, such as behavior analysis, threat intelligence software, zero-trust access control and automation.

If hackers get into one endpoint, that’s one thing. If organizations catch the threat before it spreads, they can stop the attack by taking the device off the network. But when more than one device is involved, it’s much harder to stop the attack, leaving organizations almost helpless and at the mercy of cybercriminals.

As edge networks get more complicated, AI-powered tools like behavior analytics systems and intelligent detection and response platforms are used more and more to make networks safer. These are tools that CISOs and security professionals can use to add to or supplement what they already do. They make it easier to spot unusual things and act on them on their own, so that security staff can focus on more important tasks.

A traditional approach to cybersecurity says that CISOs need a lot of people to analyze threats and respond to them, or they need to hire experts and security providers from the outside, which is expensive and takes a lot of resources. Traditional methods of cybersecurity are not only expensive and slow, but they aren’t 100% effective. This is because attacks like ransomware are so big, frequent and fast.

So, CISOs must set up an effective and smart defense against ransomware with automated, AI-based detection and response cybersecurity solutions that can stop attacks from getting into an organization’s digital environment.

AI, machine learning and automation are the best ways for organizations to stop ransomware attacks, find them and fix them. When these technologies are part of a set of tools for an advanced security solution, they automatically find and block malicious assets and strange behavior from all attack vectors (including at-home endpoints, edge networks and IoT devices). They also help security teams quickly match alerts, find real threats and use contextualized threat intelligence to prioritize risks and protect the whole enterprise.

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Serhiy Protsenko

Serhiy holds both Doctor of Science and Professor in Physics degrees. He has 20+ years of extensive theoretical and practical experience in Data Science and realized numerous projects based on AI technology.

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