- Use Cases
Monitoring and analyzing every endpoint on your network is critical to the detection of Insider Threats. However, monitoring all these endpoints creates an extremely large data set that can can become impossible for a security team to sift through manually. Ten employees might be manageable but imagine a company with a 1,000 employees. Now imagine those 1,000 employees represented by 1,000 ever changing haystacks (data sets)… and it’s your job to find anomalies.
Powered by advanced machine learning, statistical analysis, and natural language processing, Veriato autonomously creates an integrated user view, by analyzing both structured and unstructured data aggregated from various sources. This data is then used to rank risky users in a watch list which prioritizes, predicts and prevents potential threats.
Self-learning of behavioral patterns for both individuals and groups, driven by advanced machine learning, enables no-touch understanding of what normal looks like in your environment.
Import groups from Active Directory, or let the software autonomously identify groups within your organization through pattern analysis of resource and application usage..
Veriato tests against the CERT dataset and consistently has <2% false positives.
Disgruntled employees are not born overnight. Cerebral’s Sentiment Analysis uses computational linguistic analysis, to identify and categorize opinions expressed in text then determines whether the writer's sentiment towards the company is changing in either a positive or negative direction and can alert security of a possible risk.
Cerebral takes into consideration statistical anomalies and applies machine learning to them to find unique deviations from the baseline anomalies. Detecting deviations from established patterns enables early warning of insider threats. An outside attacker, no matter how sophisticated, will cause a deviation from normal behavior.
Actions and activities all play a part in building a comprehensive Risk Score for each employee on your network. The score’s are based on all online behaviors, from files downloaded to geolocation. Once an employee reaches a certain threat threshold set by your security team, an alert is sent and action can be taken.
Cerebral continually monitors all the actions on every endpoint, including files, applications, network use, email, web, geolocation, psycholinguistics, signs of compromised credentials and more. The volume of data that needs to correlated, analyzed, cross analyzed against dynamic baselines and group behaviors is enormous.
To continually assimilate this dynamic data stream coming from every user and pick out fluctuating patterns of behavior and signs of threat isn’t possible by even the best security teams. A mature insider threat strategy requires machine learning and AI.