Cyber attacks usually hit companies like a bolt from the blue: IT systems that were just working perfectly fail without warning, sensitive data is suddenly no longer accessible and production processes come to an unwanted standstill. It is only at this moment that most of those affected realize that they have been targeted by cybercriminals - unfortunately much too late. Serious damage can now hardly be averted.
In most cases, such scenarios could be prevented in advance. After all, hackers are usually out and about in the networks of the affected companies for several days before they take the infected systems hostage and make ransom demands. In order to be able to recognize the danger in this early attack phase and to be able to act in good time, companies need modern attack detection. This combines the know-how of security experts with organizational measures and technical systems in order to identify attacks during operation.
In most companies, however, such a security precaution is still sought in vain. Instead, many of them lull themselves into a false sense of security: They believe that classic security components are sufficient to prevent cybercriminals from entering the network. However, this is a dangerous fallacy. Hackers are constantly optimizing their attack methods and are constantly developing new, complex procedures with which it is alarmingly easy for them to overcome security hurdles unnoticed. Therefore, even standard components such as SIEM systems are no longer sufficient. Instead, modern attack detection is required that not only relies on static rules and use cases, but also on behavior-based anomaly detection.
Basically, the components used in it perform four steps to keep hackers at bay. First of all, a risk assessment of the existing security architecture must be carried out and an emergency plan drawn up in order to be able to act quickly and in an organized manner in the event of an attack. Then sources are defined that should be taken into account when identifying attacks. These include IT security components, endpoints or cloud apps. Then the actual attack identification takes placeusing the latest technical solutions. The latter quickly learn about the company's typical network processes and use machine learning to remember the behavior of the environment. Deviations from the "learned" normal behavior can then be shown. If an anomaly is detected, it must be qualified by knowledgeable experts in order to determine the existing risk and - if necessary - to initiate an appropriate reaction.
This combination of technical solution and manual expert analysis finally makes it possible to identify new and highly complex attack attempts that cannot be detected by classic components. In addition, there is an enormous time saving: Through the behavior-based analyzes and the use of machine learning, the attack identification is partially automated and thus accelerated.
Since the threat situation is becoming increasingly confusing and the risk of attacks is increasing in almost all industries, modern attack detection can secure the existence of many companies. After all, the effects of an attack that is detected too late are devastating in many cases. It is not uncommon for data and reputation to be lost or even production and company downtime to occur. If the attempted attack is detected and prevented in good time, the worst can be prevented. Modern attack detection is therefore essential for forward-thinking companies in today's digitized world.