Detect and Respond to Data Loss

Without the pain of endpoint DLP tools

Data continues to leak out of organisations. Status quo won’t cut it.

Protecting intellectual property, customer and employee information requires more than preventative measures. With a prevention heavy approach, you’ve got many blindspots even after spending months of data discovery, classification and policy creation. Invariably, you cannot respond to data loss in real-time and need days or weeks to correlate DLP, application and forensic logs.

Current exfiltration channels supported

  • Uploads/downloads from Web
  • Cloud sync & storage
  • Desktop & cloud email
  • Removable media
  • Cut/copy/paste
  • Command line

5 out of 300+ common risk indicators

  • Downloading sensitive files during irregular hours
  • Moving sensitive code from Github to unusual locations
  • Installing suspicious software
  • Uploading customer lists to untrusted cloud storage
  • Copying files onto unlisted removable media

Modern approach to DLP projects

Real-world Insider Scenarios

  • 350+ crowdsourced scenarios from customers & based on NIST, CERT and NITTF expertise
  • Customise real-time response from real-time user education to more stringent controls based on file sensitivity, user role & alert severity

User and Data Context

  • Track files from their origin to final destination, through renaming and movement by users across endpoints
  • Gain immediate context into user and data activity via timeline or visual replay

Without endpoint crashes

  • Ensure users can complete their jobs securely with sensitive data through a lightweight, user mode agent and silent deployments
  • No conflicts with your endpoint security stack when deployed enterprise wide

Ready to see how ObserveIT helps you detect and respond to data loss in real-time?

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Bain Capital

We are continually confronting emerging risks involving data security — and insider threat is clearly one of them. We consider our data - our intellectual property and market knowledge - to be a significant business advantage in this competitive world. ObserveIT serves as an information protection guardian.

-Mark Sutton, CISO, Bain Capital

2000+ Customers

  • Baxter
  • Boston Private Bank & Trust Company
  • CGI
  • Corning
  • Cree
  • EY
  • First Foundation Bank
  • Genpact
  • iconectiv
  • Just Eat
  • Microsoft
  • ptc
  • Rogers
  • RMS
  • Security Mutual Life
  • SKY
  • TIAA
  • Visa

Learn how Bain Capital uses ObserveIT to protect against data loss:

Challenge

Data loss blindspots aplenty with legacy DLP tools

This organization recognized that traditional data loss prevention tools (DLPs), designed to regulate data exchange via a complex set of rules and alerts, were not sufficient to stymie a rising tide of insider threats. The tools were difficult to use and not designed for data leakage prevention, in spite of their name. Additionally, they were relatively easy for users to bypass. The organization needed more visibility into user activity to effectively detect and stop data leakage.

INDUSTRY: Asset Management

SIZE: 500 employees; global workforce

Solution

User context leads to faster investigations

ObserveIT’s insider threat management platform offers more visibility than DLPs, along with a lighter weight agent. With ObserveIT, the organization is able to quickly identify risky user activity, including unauthorized removable media usage, print jobs, and file copying.

Result

Secure sensitive data without operational headaches

Identifying these risky activities and correlating them with broader data movement enables this organization to stop data leakage in its tracks and dramatically decrease risk. The ability to collect context regarding user behaviors that could indicate risk of data loss was a “game-changer” for this organization. Now the infrastructure and compliance teams can achieve their security objectives and prevent data leakage without slowing down end users.

Learn more about detecting and responding in real time to data loss threats

Watch the 2019 Forrester Webinar