CloudLock Announces New Threat Analytics Powered by Machine Learning

WALTHAM, MA--(Marketwired - Jun 23, 2016) - CloudLock, the leading provider of Cloud Access Security Broker (CASB) and Cybersecurity-as-a-Service solutions, today announced the release of the next generation of its innovative machine learning capabilities to include suspicious login activity monitoring, location-based anomaly detection, and IP reputation analysis to identify anomalies, zero in on suspicious behavior, and pinpoint true threats across SaaS, IaaS, PaaS, and IDaaS cloud platforms. The inability to detect real threats from millions of alerts they receive daily as well as the lack of timely response capabilities are the greatest challenges facing security teams today. Pioneered by CloudLock's research intelligence arm, the CyberLab, machine learning capabilities are the foundation of the Cloud Security Fabric, helping security teams narrow their focus on user activities indicative of true threats. Using the company's Cloud Threat Funnel methodology, along with big data technologies and multiple advanced clustering algorithms, CloudLock's machine learning technology continuously evolves based on analyzing the industry's largest data set spanning over one billion files and events monitored daily. CloudLock's expanded machine learning capabilities include: Suspicious Login Activity Monitoring captures high frequency login anomalies, such as login failures and login challenges from unusual devices, geographies and time periods for a given user, indicate potential threats to corporate user accounts.