Security information and event management (SIEM) is a crucial tool that offers real-time monitoring and analysis of security-related events as well as tracking and logging of security data for compliance or auditing purposes. SIEM plays an important role in identifying security incidents and helping IT and security teams respond effectively.
However, as threats become more sophisticated, SIEM solutions must evolve to keep up. The future of SIEM lies in predictive analytics and machine learning, which can help organizations prevent attacks before they occur.
What is predictive analytics?
Predictive analytics is a type of advanced analytics that uses statistical modeling, data mining techniques and machine learning to forecast future outcomes based on historical data. Companies use it to identify risks and opportunities by finding patterns in data.
Predictive analytics is linked with big data and data science. Nowadays, organizations have a large amount of data in different repositories, and data scientists extract insights using deep learning and machine learning algorithms. Techniques such as logistic and linear regression models, neural networks and decision trees are used to make predictions. These modeling techniques use initial predictive learnings to make additional predictive insights.
SIEM with predictive analytics vs. traditional SIEM: The major differences
The largest application of security analytics lies in its crucial role in threat monitoring and incident investigations, according to a paper presented at the National Conference on Information Assurance (NCIA) in Pakistan.
Its primary focus is on the discovery and comprehension of both known and unknown cyberattack patterns. This capability is expected to ha ..
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