Security Information and Event Management (SIEM) software has become a go-to solution in the network security field.
While it’s true that having a SIEM is better than forgoing network monitoring all together, a standalone SIEM solution is simply insufficient in today’s cybersecurity landscape. Hackers and other bad actors have become more sophisticated — many of today’s cybercriminals can easily outsmart a standard SIEM setup. Advancements in Artificial Intelligence (AI), in particular, have given rise to the next generation of network security threats.
Today’s high-tech risks require high-tech solutions.
SIEM Overview, Limitations, and Vulnerabilities
SIEM software compiles log data generated from specific points within a network. Data sources can include SecOps features like firewalls and antivirus filters, host systems, and applications.
The SIEM software then analyzes the collected data and flags potential security incidents and events. While SIEMs can vary in functionality to some degree, in general, some of these instances are included in a system-generated report, while more immediate issues trigger an alarm.
Network security teams respond to alarms as they occur, and review the SIEM-created log reports later.
Limitations and Vulnerabilities
The inherent nature of SIEM software includes several limitations and vulnerabilities.
SIEM technology requires a great deal of human interaction. SecOps teams need to review reports, respond to alarms, and keep the software updated. Some SIEM functions are automated, for example, halting some network functionality in the event of an apparent security breach, but these automations are limited in scope and effect.
Even in the best-case scenario, SIEM technology requires hours of manual review, opening the enterprise up to a higher risk of human error and a hit to efficiency.
SIEM effectiveness is limited by the amount of information the software can log and analyze, but the software can’t actually log everything. SIEMs routinely miss significant security threats.
Logged Data Risks
Bad actors can attack SIEM-generated logs. In fact, these logs have become a frequent target for hackers, who access the logs to steal and destroy data.
SIEM software can’t keep up with dynamically changing data. It must be updated manually to monitor constantly evolving networks. Third-party networked devices, including increasingly-popular BYOD and IoT devices, introduce increased vulnerability with each new addition.
Advantages of Machine Learning AI Network Security
MixMode harnesses the power of unsupervised AI to create intelligent, comprehensive network security monitoring that responds to security events immediately, accurately, and automatically.
MixMode’s third wave AI capabilities can save time and energy your SecOps team can spend on other important initiatives.
· Continually monitors network evolution, comparing it against an AI-created baseline.
· Requires far less human input and interaction, reducing human error and increasing efficiency.
· Context-aware intelligence results in far fewer false positives.
· Baseline is created in a few days vs. several weeks for other AI-enhanced platforms.
· AI-prioritized reports significantly reduce time spent sifting through SIEM logs
· Comprehensive network monitoring robust enough to handle the enormous tech stacks enterprises must manage today
Mixmode can be used alongside an existing SIEM, as well, creating overlapping security measures that eliminate gaps.
Set up a demo with a Mixmode network security professional who can identify how third wave AI can enhance your system monitoring.
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*** This is a Security Bloggers Network syndicated blog from MixMode authored by Ana Mezic. Read the original post at: https://mixmode.ai/blog/yesterdays-siem-solutions-cant-combat-todays-cyberthreats/