UEBA: A recipe for better security

User and entity behavior analytics (UEBA) is an artificially intelligent process that uses large amounts of data to predict human and machine behavior.
UEBA-header
Challenge

Current challenges

  • Many solutions cannot adequately link data to the perpetrating user.
  • Packet capture is limited in providing necessary context for determining how apps are used.
  • Not all companies can afford white glove cyber-analysts to keep network security up to date.
  • Hidden and encoded data can hide what a user actually does on the network.
  • It is challenging to determine whether an action is unintentional or malicious.
Solution

Why Next DLP

Hybrid Cloud-Edge Deployment - Next DLP machine learning operates on both a global scale and localized within the network, so analysis is scalable, reduces false positives, and eliminates latency.

Dual analytics - Machine learning is not infallible, but combined with unconventional dynamic analytics, non-standard attacks can be stopped using both sophisticated artificial intelligence and static rules.

Decisive inference - Next DLP's solution can sequence events, test and weigh attributes simultaneously, and adopts top-level, decision-analysis structures.

Adaptive learning - Continuous recalibration and creative policy suggestions means better machine learning

Group 47 Copy

Remote security

Get full visibility of your data and endpoints outside of the office. Reveal Cloud notifies organizations of unencrypted or unapproved Wi-Fi networks, suspicious login activity, or printing outside office hours, geolocation, and more.

Group 50

Authenticate users

Organization-owned computers are increasingly used for both work and personal use. Using advanced mechanisms such as keystroke analytics, Reveal Cloud can detect when computers are shared with others, and even spot credential sharing.

Group 64

Privacy-friendly insider risk solution

With Next DLP's industry-leading solution of pre-built data minimization techniques, such as pseudonymization and anonymization (partial and full redaction), you can now detect and mitigate threats while maintaining the confidentiality of users.

Group 48

Policy and machine learning

Combining these two powerful methods of detection, regardless of an employee being offline or remote. This combination analyzes more user scenarios and detects more incidents–resulting in faster threat remediation.

Group 62

Out-of-the-box and configurable policies

Built-in policies for data tracking, cyber hygiene, and malicious activity that can detect and defend against various risks. Policies run against computers and users, providing insight into how users access files, applications, and systems, which determine specific areas of risk.

Group 47

Flexible, seamless deployment options

The Reveal Cloud agent is deployed to Windows, macOS, and Linux computers and servers, where it collects granular behavioral information for security threat analysis.

 
Demo

See how Next DLP protects your employees and prevents data loss