Bay Dynamics, a cyber-risk analytics provider, recently announced a technology partnership with Symantec that will enable enterprises and government agencies to more efficiently and effectively detect, quantify, and prioritize insider threats and outside risks.
Symantec is integrating Bay Dynamics’ flagship analytics platform, Risk Fabric software platform, with its Data Loss Prevention (DLP) offering and other technologies, creating a central point of behavioral analytics that dynamically delivers top mitigation actions to help prevent malicious insiders from stealing sensitive data.
Bay Dynamics’ and Symantec’s technology partnership enables organizations to:
Use Bay Dynamics’ behavioral analytics and machine learning technology across all user-data activity tracked by Symantec DLP, with on-premises data being protected with traditional DLP, cloud DLP with Symantec CASB, and mobile devices being protected with Symantec Information Centric Encryption.
Help connect the dots between user accounts, confidential data, and corporate assets to understand user behavior and further manage enterprise risk.
Identify the top insider threats to enterprise data assets that, if compromised, could cause significant financial damage.
Prioritize and prescribe actions IT administrators should take today to protect corporate digital assets that matter most.
Break down the barriers between siloed cyber security tools so that contextual data is aggregated and analyzed on a single platform.
Launched in 2013, Risk Fabric is an analytics software platform that dynamically calculates the financial impact of cyber risk based on actual threats and vulnerabilities in the environment. The platform integrates with more than 30 cyber security tools, adds proprietary user and entity behavior analytics, and delivers critical information regarding the top threats and vulnerabilities facing an organization to a wide array of stakeholders responsible for risk mitigation. The Risk Fabric platform also measures how much risk was reduced due to actions taken.