Over 85% of all data breaches involve the use of stolen or compromised user credentials. These credentials, usually in the form of user login IDs and passwords, are the key to a criminal’s ability to gain unauthorized access and penetrate an organization’s network. This is especially true when credentials are associated with privileged accounts. It’s therefore critical to detect and prevent their theft and subsequent use.
Since they appear to be valid, stolen or otherwise compromised credentials are very difficult for most security systems to spot. Only an advanced understanding of the attacker’s actions and behavior can identify unauthorized access from an imposter at work. Unfortunately, SIEM and access management solutions don’t have the sophisticated correlation tools necessary to distinguish between good and bad behavior, so they can’t detect modern credential theft attacks.
Fortscale uses numerous advanced behavioral models and machine learning algorithms to ingest, enrich, and model massive amounts of data from SIEM, access, and authentication systems. The resulting behavioral intelligence detects compromised credential activities and other anomalous actions that allow unauthorized access. Modeled suspicious behaviors, such as impossible geographic appearances, irregular usage of resources, or unusual methods of access, are all indicative of compromised credential activity and are used to trigger meaningful security alerts on Fortscale’s dashboard.