Published on the 1st December 2020
Ransomware, destructive malware, insider threats, and even honest mistakes present an ongoing threat to organizations that manage data in various forms. Database records and structure, system files, configurations, user files, application code, and customer data are all potential targets of data corruption and destruction. A timely, accurate, and thorough detection and response to a loss of data integrity can save an organization time, money, and headaches.
Once AI has effectively identified the potential risks and threats, the next step is to prioritize what gets addressed and in what order.
“Since AI-based detection systems usually work with uncertainty, they are useful not only to raise alerts when something seems to be wrong but also to give a score on how close a given event is from a cyberattack,” says IEEE Member Marcos Simplicio. “Events with higher scores can then be prioritized accordingly. For example, above a certain score, automated measures may be taken to stop the highly probable attack without any human intervention. Events with a lower score can then be forwarded to administrators for further analysis, and some events with low scores may be simply logged without any additional action, since handling them may not be worth the effort.”