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Bay Dynamics Launches Risk Fabric Portal to Identify Threat Patterns

28
Jan
2014

Bay Dynamics, a provider of information risk intelligence solutions, today launched a new portal designed to help customers identify threat patterns, trends and behaviors based on deviations from normal user activities.

Called the Risk Fabric Remediation Portal, the feature is a component of the company’s user and information oriented analytics platform, and helps organizations search out the root cause of an incident and take action to avert threats and prevent security events from recurring.

The Remediation Portal is able to work with large data sets and can take automated actions or provide guidance for remediating these threats, the company said.

“Today, organizations are drowning under their big data initiatives, lacking the ability to effectively filter and analyze the information in a timely manner to support their protection efforts,” said Feris Rifai, CEO of Bay Dynamics. “With the Risk Fabric Remediation Portal, we are putting all of this information through a user-centric lens for greater clarity and context. This unique approach elevates security incident and event remediation to focus on dealing with patterns, trends, and behaviors.”

The Risk Fabric Remediation Portal enables technical staff to conduct forensic analysis, remediate security events, and accelerate incident response. Additionally, it helps customers identify broken processes and accept or reject the risks associated with the handling of their own data. The platform also helps CIOs and CISOs better understand the effectiveness and implications of broader protection efforts.

According to San Francisco, California-based Bay Dynamics, the solution can leverage machine learning and behavior analysis capabilities to maintain intelligence about problems and proper categorizing, prioritization, and resolution.

Additionally, it automatically enriches and adds to the basic security event data generated by network and endpoint tools, adding the relevant context (for example: users, accounts, account attributes, machines and machine attributes) to detect threats, including organized or collusive activities.

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