Using event correlation and AI for Threat Detection and Incident Responsegiusel
According to Cybersecurity Ventures, cybercrime will cost the world $10.5 trillion annually by 2025. However, it is alarming how many companies are unaware of the aftermath of being attacked.
A successful attack can cause irreversible damage to companies’ finances. This is because attacks include money theft, damage, and destruction of data, interruption in services, decreased productivity, theft of intellectual property, theft of personal and financial data, reputational harm, and others.
So, how can a company effectively identify any malicious activity that could compromise the network?
How can a company quickly identify a cyber-attack, minimize its effects and reduce the risk of future incidents?
Today, there are innovative solutions named SIEM (Security Information and Event management) that include AI-powered event correlation for threat detection and incident response in real-time.
Event Correlation tool and Artificial Intelligent
The event correlation in SIEM solutions allows aggregates and analyzes log data from across the network applications, systems, and devices. This characteristic guarantees to discover security threats and malicious patterns of behaviors in corporate networks that otherwise go unnoticed.
The event correlation process can be broken down into three steps:
- Detection: Identifying and categorizing events
- Analysis: Analyzing the events for patterns
- Response: Acting on the patterns detected and taking appropriate measures.
To improve the accuracy of event correlation, many organizations are adopting a combination of machine intelligence and human intelligence to do so. They have realized that no single approach can be successful on its own. Therefore, it points a before and after in cybersecurity’s advances, the use of SIEM tools that incorporate correlation engines, machine learning algorithms, and artificial intelligence.
Cyber security threats evolve continuously, and trying to predict them is almost impossible. However, most of them can be detected in real-time with machine learning algorithms and artificial intelligence. The machine learning algorithms allow the system to learn from the environment and gain the ability to identify abnormal and threatening behavior.
UTMStack AI-powered correlation engine
UTMStack is an excellent cost-effective SIEM product example, also affordable for small and midsize businesses. Instead of using only hardcoded correlation rules and signatures, UTMStack analyzes the environment and defines custom rules. Its threat detection engine comprises several rule-based correlation systems, scanners, and AI-powered machine learning algorithms. The Network and Host Intrusion Detection Systems are based on rules and heuristic analysis with ATP capabilities, analyzing the network traffic, protocols, and DNS. The platform also includes the options of building customized dashboards or using existing ones to monitor, analyze security data and respond to incidents in real-time.
Logstash is an open-source data collection engine used to dynamically unify data from disparate sources and normalize the data into destinations of your choice. UTMStack uses Logstash to parse logs from different sources (firewalls, AWS, O365, etc.). Then, these logs are transformed with input, filter, and output plugins, through many native codecs, further simplifying the ingestion process. Once you apply filters in data parsing, the logs are sent to the UTMStack correlation engine, applying several rule-based correlations to generate alerts and normalized logs.
As long as organizations are connected to the internet, there is a chance of being hacked. However, implementing the best cybersecurity practices and a SIEM event correlation tool that automates threat detection and incident response through AI-powered machine learning algorithms will make their infrastructures and assets safer.