Data Breach detection requires analysis of security related data from multiple angles – file usage, user activity and network traffic (applications, servers). Additionally, threat intelligence and vulnerability scan result plays important role in enriching context, particularly when external perpetrators are involved.

CloudConsole relies on all these datapoints while applying Machine Learning to create a baseline profile and determine deviations with a great degree of confidence. Potential activities, such as port scan, leading to a breach can be correlated in near-real time thus minimizing the scope and scale of breach, saving organizations from economic and reputation loss.

Detecting ransomware requires a high degree of reliance on behavioral analytics and threat modelling to follow various stages of its propagation. Traditional threat detection tools rely heavily on the security analyst to discern unusual activities and create correlation rules to stitch indicators together. This approach heavily weighs on the analyst’s skill in threat detection and fluency with a complex UI, thus leaving a factor of subjectivity in reaching the desired outcome. In sharp contrast, CloudConsole’s aiSIEM/aiXDR leans into Machine Learning for behavioral analytics and Artificial Intelligence for correlating indicators driven by built-in dynamic threat models. When these indicators line up with a certain degree of confidence, the algorithm raises an alert for the analyst to act upon.