NEWS: Logpoint Acquires Muninn
We know that visibility of all actions taken within your network is key to successful cyberdefence. In recent years, security tools and programs have made significant advancements in detecting and preventing advanced security threats worldwide. However, can too much of a good thing become bad? With a shortage of cybersecurity staff and an overwhelming number of notifications and alerts, new challenges arise.
A poll conducted in 2020 asked 427 security professionals about the volume of alerts at their companies. A staggering 70 percent reported that their alerts had more than doubled in the last five years. What's more, a total of 93 percent claimed that they couldn't address all the alerts in a single day*. This data paints a clear picture: the cybersecurity world is experiencing alert fatigue.
Alert fatigue is not just a term confined to the world of cybersecurity. It's a phenomenon that's prevalent in various fields, including intensive care units (ICUs). With the advancements in technology and limited resources, nurses in ICUs are bombarded with an overwhelming amount of information that they don't have the time or resources to address. Similarly, in the cybersecurity world, alert fatigue is a state of mind where security analysts receive too many alerts or false positives from various sources like device monitoring, email filtering, internet security, network firewalls, and more. The never-ending list of potential threats makes it challenging for security teams to identify and respond to actual security risks and threats.
The above mentioned survey revealed that 99% of security teams reported multiple issues related to receiving high volumes of security alerts, including*:
To address this challenge and combat alert fatigue, organizations can implement several key strategies. The first strategy is reducing the volume of alerts by filtering and prioritizing them. By streamlining the alerts, analysts can focus on the most critical issues. The use of artificial intelligence and machine learning, like Muninn Detect, can automate this process and ensure that only the most important alerts are flagged, saving time and increasing efficiency.
Another important strategy is improving the accuracy of alerts. False positives are a major contributor to alert fatigue. They can cause analysts to become desensitized to notifications and disregard them, which can result in overlooking the real threats. Implementing more advanced detection techniques, such as behavior-based detection systems, can help reduce the number of false alarms. These systems can identify patterns and anomalies in user behavior, making it easier to pinpoint genuine threats. On top of that organizations must provide their security analysts with the tools and resources they need to manage their workload effectively. For example, implementing a centralized dashboard can help prioritize and manage alerts efficiently. This approach provides a comprehensive view of the security landscape, enabling analysts to focus on the most critical issues.
In today's rapidly evolving cyberlandscape, ensuring that security analysts are well-equipped with the necessary skills and knowledge to manage and respond to alerts is more important than ever. Analysts need to be equipped with the skills and knowledge necessary to manage and respond to alerts effectively and manage their workload. In addition to training, automating incident response processes can be a powerful tool in the fight against alert fatigue. By automating routine tasks such as malware detection and containment, security analysts can focus on more complex threats and investigative work. This not only reduces the risk of human error but also ensures consistent and effective incident response.
We have analyzed three customer cases to demonstrate how Muninn can effectively minimize noise and false alarms, providing only relevant alerts to the IT security manager. Our evaluation included examining the most frequent high alerts prior to implementing Muninn and comparing them to the alerts received a few weeks after implementing our sensors and establishing a baseline.
