SOC Insider Threat Security Analytics
As cyberattacks continue to grow in both number and sophistication, and the stakes grow higher as threat surfaces expand, organizations are under intense pressure to protect themselves from compromise. Security leaders face a perpetual challenge to keep up with ever-evolving hacker tactics that easily elude signatures, rules, and patterns in traditional cyber defense systems. Further complicating the challenge is the need to protect hybrid environments of on-premises and cloud.
User and Entity Behavior Analytics (UEBA) has emerged as the most effective approach to comprehensively manage and monitor identity-based risks and unknown threats across all of an organization’s environments. UEBA security draws from the context of big data and is driven by machine learning models rather than signatures or rules to deliver invaluable visibility and risk scoring of suspicious activity.
Gurucul UEBA uses algorithms and machine learning to detect anomalies in the behavior of users and non-human entities such as the routers, servers, endpoints, and other devices in a network. UEBA looks for unusual or suspicious behavior that deviates from a baseline of normal everyday patterns or usage. For example, if a particular user typically logs into the network from an IP address in Atlanta, and on a given day that same user credential logs in from both the address in Atlanta and an IP address in Los Angeles within a two-hour window, the UEBA security system would consider this an anomaly. An alert can be sent to a security administrator, or if automations are in place, that user can be automatically disconnected from the network pending further investigation of the situation.
The “entity” part of the solution means it also monitors devices that are part of the network. Machines, like people, can exhibit unusual behaviors that may indicate an attack is underway. For example, a desktop device might be observed to be communicating with an unusual IP address that external threat intelligence says is a malicious site. Prompt detection and alerting of this behavior can lead to quick mitigation such as blocking the traffic at a firewall to prevent outreach to that IP address.
UEBA quickly identifies anomalous activity, thereby maximizing timely incident or automated risk response. The range of use cases is what makes Gurucul UEBA extensible and valuable. For organizations to effectively face their cybersecurity challenges, they must assure the use cases align with their specific needs and varied requirements today and into the future.
Gurucul provides a comprehensive set of use cases for User and Entity Behavior Analytics. Below are the top UEBA use cases that power modern, next-gen Security Operations Centers (SOCs):
Gurucul also offers several industry-specific pre-packaged analytics to address Healthcare, Finance, Government, Retail, Manufacturing, and Insurance use cases. These sets of models are focused on addressing the challenges and threats unique to each industry vertical. This helps reduce any customization or implementation effort to build industry-specific models from scratch. These models are developed in partnership with the Gurucul Labs team, technology and channel partners, and customers, taking into consideration telemetry from specialized systems, fraud / threat scenarios, and standards.
Having a broad selection of UEBA use cases provides customers with the assurance that their advanced security analytics requirements will be addressed. The overall benefits of security analytics include:
