Uncover Malicious Insiders

Malicious insiders and compromised entities are constant and persistent data security challenges, driven by factors including IT complexity and increased cloud adoption. Bay Dynamics Risk Fabric was purpose-built to integrate with distributed security infrastructure and user data repositories to provide the end-to-end visibility needed to identify and respond to insider threats.

icon-magnify The Insider Complexity Conundrum

The sheer complexity of isolating insider threats within the context of today’s ever-changing business processes and data handling requirements is daunting. Numerous layers of technical controls needed to identify emerging issues, including disparate security infrastructure and user access management systems, further intensify the problem. For many organizations, preventing abnormal and malicious activities without getting in the way of legitimate business practice has become a hugely intensive task.

icon-triangle Integrated Analytics that Prioritize Threats

Dedicated analytics that integrate with existing security infrastructure and policies to triangulate key information regarding users, data interaction and behavior across numerous sources offer a critical breakthrough in addressing insider threats. Through centralized analysis and visualization, backed by machine learning and peer-based comparison, truly abnormal incidents can be escalated and stopped.

Bay Dynamics Risk Fabric integrates with every relevant security platform to isolate those users and entities that represent material threats, including systems compromised via malware attacks, to enable rapid, automated response that mitigates exposure of protected data and assets.

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Detailed comparative analysis of user activities and threats

WHITE PAPER: Machine Learning for UEBA

Strategic Workflows

icon Risk Scoring
icon Remediation Recommendations
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icon Trending and Metrics
Integrated analysis that combines user and security data to create a behavior-centric view into existing alerts helps prioritize and escalate those incidents that truly demand investigation.
Bay Dynamics Risk Fabric integrates with DLP, endpoint and cloud security tools, Active Directory, HR systems, and other sources, to understand where user profile data [ex. recently terminated employee] intersects with policy violations to determine problematic incidents.
Today’s distributed and disparate security infrastructure has created an environment where nearly every platform maintains unique policies that represent only some aspect of potential risks.
Bay Dynamics Risk Fabric provides centralized analysis and visualization across numerous channels [ex. multiple DLP or endpoint implementations] to offer detailed insight into those insider threats that transpire across widespread platforms and policies and might go otherwise unnoticed.
One of the most significant hurdles to pinpointing insider threats are the massive volumes of alerts that analysts must scrutinize to isolate potential attacks, many of which represent non-malicious incidents.
Bay Dynamics Risk Fabric leverages advanced machine learning and comparative analysis to both establish a baseline for identification of truly abnormal behaviors, and constantly learn from ongoing analyst input, allowing for escalation of real world threats and bulk handling of high-volume incidents.

 

Understanding how and where to enact targeted actions that limit further risk exposure is another key element to both mitigating threats and minimizing all potential insider threat security impacts.
Bay Dynamics Risk Fabric offers a highly adaptive and targeted kill-chain type analysis and remediation interface that allows practitioners to identify the specific point in any chain of events that represents the most strategic opportunity for threat remediation to undertake actions that address specific risks.

CASE STUDY: Real-World Success

Learn how a global payments leader leveraged Risk Fabric alongside its existing security infrastructure to identify and mitigate inside threats, without adding related headcount.

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