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How AI Revolutionized Traditional SIEM Technology

Security Information and Event Management (SIEM) is a crucial tool that provides cybersecurity teams with a wide range of functionalities to monitor IT environments. These functionalities include data analysis, event correlation, aggregation, reporting, and log management.

However, despite its importance, SIEM has yet to evolve beyond its primary role of providing a better and more searchable rule-based log engine. A study states that 88% of companies have problems with their SIEM platforms, and 99% want more SIEM automation. 

With the growing complexity and frequency of cyberattacks, security information & event management (SIEM) solutions are becoming more critical than ever. This is where AI-powered SIEM systems come into play. AI’s adaptability and advanced capabilities permit it to be used in a variety of ways, and by integrating AI with SIEM systems, the efficiency of data analysis, vulnerability management, and threat management software can be increased exponentially. Artificial Intelligence for IT Operations, or AIOps, is a term that was coined by Gartner in 2016 to describe the application of AI and machine learning technologies into IT operations. The purpose of AIOps is to improve IT operations by automating the identification and resolution of IT issues.

The integration of AI and Machine Learning-based technologies with predictive analytics in SIEM solutions has led to the emergence of AI-powered SIEM systems. These systems gain deep learning capabilities and a plethora of integrated tools, allowing for more informed outcomes and real-time threat detection, analysis, and response.

The advantages of an integrated SIEM are as follows: 

Preventing Stealth Attacks  

A typical SIEM correlates events from several sources acquired over a short period of time. AIOps systems collect event data over a long period (years), store it in a database, and then apply analytics to it. AIOps can use this data to change the infrastructure baseline and warning levels over time and perform some remedial steps automatically. Using big data allows SIEM to detect even the slowest or stealthy network actions that it would generally overlook as a one-off.

Noise Elimination 

A typical SIEM generates a large amount of monitoring data/logs. However, SIEM reports should be clearer and contain less noise for better comprehension. An AI-integrated SIEM solution efficiently manages big data and easily automates redundant, tedious activities.

Detecting Threats 

AI and machine learning technology can include threat intelligence feeds and conventional log data. If your SIEM has continuous access to one or more threat intelligence feeds, machine learning technologies can utilize the context it provides. As it learns more, it begins recognizing dangerous behavior warnings beyond the data given at the outset. It enhances the SIEM’s decision-making ability, specifically in terms of accuracy, and provides cybersecurity solutions for previously unknown threats.

Pattern Prediction 

Machine learning algorithms enhance SIEM systems, allowing them to predict and anticipate future data based on existing patterns. Consider some data patterns exposed during a security breach. Machine learning skills allow systems to internalize patterns and then utilize them to detect suspicious activity that could indicate a subsequent attack.

Improving Team Performance 

Any organization’s SOC teams are limited in their capacity to handle the enormous amount of log data generated by SIEM, which often includes irrelevant information. The constant flow of alerts can cause alert fatigue among SOC teams. ML-provided automated and standardized processes can lower the risk of human error while speeding up the process.

Finding Blind Spots 

As a business grows, it becomes increasingly vulnerable to the emergence of blind spots. Blind spots can often go unnoticed for months or even years. AI in SIEM can enhance network visibility, revealing blind spots quickly and consistently.


Gartner predicts that by 2023, $175.5B will be spent on cybersecurity services and risk management. According to Zion Market Research, spending for AI-based cybersecurity consulting services will reach a whopping $30.9B in 2025. Undoubtedly, artificial intelligence and machine learning technologies will revolutionize the field of cybersecurity in the coming years.

If you are seeking a comprehensive solution for all your cybersecurity needs, you have found the right partner. Sattrix Information Security offers cybersecurity consulting services that thoroughly assess and understand your concerns, and offer tailored solutions to help you achieve your ideal technological cybersecurity requirements. We expand on the traditional box-centric approach to consultative security by incorporating advanced threat analytics, SIEM, UEBA, DAM, PAM, SSO, EDR, SOC, and other technologies. Contact us now to protect your business.

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