5 Top Benefits of UEBA and Machine Learning
Organizations need to approach cybersecurity threats using an in-depth security methodology. This approach leverages layered security that provides[…]
User and Entity Behavior Analytics (UEBA) is the application of machine learning and security research to determine when users or entities are acting in unusual and risky ways.
Good UEBA doesn’t require static, predefined rules to detect threats, and can therefore evolve along with new techniques enabling your SIEM to be more efficient and effective.
UEBA uses machine learning and data science to gain an understanding of how users (humans) within an environment typically behave, then find risky and anomalous activity that deviates from their normal behavior that may be indicative of a threat.
The data breach at Capital One that exploited a vulnerability in the cloud reported a few weeks ago was one of the largest-ever bank data thefts. We look at how it maps to the MITRE ATT&CK framework and how it could have been detected.
The applications of a properly architected analytics platform are numerous. We look at the experiences of a major global airline that uses Exabeam primarily for enterprise security also leverages analytics to solve problems ranging from fraud to operations.
Many vendors claim to offer user and entity behavior analytics (UEBA) capabilities, but a variety of implementations make comparative evaluations difficult. Find out the top 10 criteria for evaluating an effective UEBA technology to guide the selection of the right solution for your business.
For cybersecurity teams, getting in front of security threats is a top priority. But with so many potential threats and adversaries, putting in place appropriate threat detection can seem a daunting task. Breaking down threat detection and a response to the most basic elements can bring that clarity.