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\u201cLearning is any change in a system that produces a more or less permanent change in its ability to adapt to its environment\u201d \u2013 Herbert Simon. Machine learning is the study of algorithms that improve their performance, with given tasks and with experience. The automation tools we use for Security Orchestration Automation and Response – provide procedures that can be used to detect and process security threats. Using SOAR and AI, a fully or semi-automated response takes place, which we can adapt to the wishes of our customers. SOCs like OSCAR \u00a9 provide an integrated security management platform that leverages automation and machine learning. They enable the detection of security threats in real time and respond to them with intelligent measures, either fully automatic, semi-automatic or even manual.<\/p>\n
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With OSCAR, we coined the term \u201cbusiness security\u201d for the first time. By business security we mean that employees outside of a company’s cybersecurity department receive added value for the business through the SOC. To this end, we have equipped OSCAR with self-learning AI-OT sensors, which allow more information used by the business to flow into the SOC and be retrieved. The SOC thus reliably monitors the OT. We can currently cover the rail, safety, energy and medical industries. We are constantly looking for more AI-controlled sensors in the world to expand our offering.<\/p>\n
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OSCAR \u00a9 strives to improve enterprise vulnerability management by providing a centralized platform powered by AI and SOAR technology and powered by machine learning. This enables a proactive and efficient security strategy based on continuous learning.<\/p>\n
The Next-Generation Security Operations Center (NG-SOC) supports companies robustly and efficiently in the following points:<\/p>\n
OSCARs \u00a9 SOAR into the existing SIEM system (Security Information and Event Management) enables seamless data collection and processing. This means that security information is not only collected and processed, but can also be managed directly from the SOAR platform. This reduces redundancies and improves efficiency in monitoring and responding to security incidents. A central aspect in the development of OSCARs \u00a9 is maintaining data economy. Targeted data collection and processing ensures that only relevant information is used for security analyses. This not only contributes to efficiency, but also to compliance with data protection regulations and minimizing potential risks when handling sensitive data. OSCARs \u00a9 uses intelligent algorithms to analyze security data, including detecting anomalies and potential threats. These algorithms enable proactive identification of security risks and faster response to events in real time, improving organizational security and minimizing the impact of security incidents. In addition to the automated response to security events, OSCARs \u00a9 also informs those responsible about incidents, measures taken and their success. This transparent communication supports effective collaboration within the security team and enables the security strategy to be quickly adjusted if necessary.<\/p>\n
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\u201cMachine Learning is the field of study that gives computers the ability to learn without being explicitly programmed\u201d \u2013 Arthur Samuel (1959). OSCAR \u00a9 strives to describe every pattern-based use case as machine learning with an impressive accuracy of 99.8% -based use case to train or further develop. This is done by continually learning and adapting algorithms to new data and threat patterns, continually improving the efficiency and precision of security operations. OSCAR\u00a9’s Dual Threat Detection (DTD) provides the flexibility to turn the autopilot on or off as needed, similar to an airplane. This feature allows security teams to intervene manually or automatically depending on the situation, maintaining granular control over security measures.<\/p>\n
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<\/div>\nThe ML training models that we use are the classic training models for supervised (inductive) learning, unsupervised learning, semi-supervised learning and reinforcement learning, as well as inverse reinforcement learning. We primarily use self-learning AI training models. Our ML experts are familiar with the thousands of machine learning algorithms that now exist and only select the best algorithms for our purposes. Our data scientists and ML experts work with our security analysts to design the optimal learning model and train the model when available algorithms and training models do not achieve the desired goal. We have quality assurance for this process.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
MACHINE LEARNING AND SOAR AUTOMATISMS \u201cLearning is any change in a system that produces a more or less permanent change in its ability to adapt to its environment\u201d \u2013 Herbert Simon. Machine learning is the study of algorithms that improve their performance, with given tasks and with experience. The automation tools we use for Security … <\/p>\n