From a cybersecurity regard, logs can act as an indicator that something is not right. Regular log analysis can help identify suspicious activity on a network, however, given the amount of log data generated by networks, it is impractical to review all logs manually on a regular basis.
Log monitoring software (SIEM) takes care of that task by using rules (we’ll come back to this) to automate the review of logs to only highlight events that look like potential threats.
SIEM solutions attempt to do this on a real-time reporting basis.
SIEM
When companies wish to implement a SIEM solution they need to provide the SIEM with a set of rules that direct its attention to the most vulnerable areas of the network. As a SIEM is built on AI or Machine Learning, the SIEM will take several months of analysing data to learn about the network it is trying to protect and it will have challenges, what if there are adversaries already on the network who are using compromised user accounts and so look perfectly legitimate? How will the SIEM see these as a threat?
As SIEM delivers a real-time assessment on a network it takes a very narrow snapshot of the logs in play.
Compromise Assessment
GuardYoo’s Compromise Assessment helps IT Teams shorten the implementation period of a SIEM by providing a “map” of the company’s attack surface.
Because GuardYoo delivers a retrospective analysis of log data that is up to 9months old, it takes a much wider and deeper look (not real-time) at what has been happening on the network.
By using proprietary Machine Learning algorithms, GuardYoo will identify patterns, anomalies, threats and weaknesses, that can help design rules that can help get a SIEM working at its maximum capability in a much shorter period of time.
If this information is helpful to you read our blog for more interesting and useful content, tips, and guidelines on similar topics. Contact the team of COMPUTER 2000 Bulgaria now if you have a specific question. Our specialists will be assisting you with your query.
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