Dropbox

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Find Risks in Dropbox Accounts

Protect sensitive customer information and documents stored in your Dropbox account from data loss due to accidental or malicious use by employees or account takeovers by hackers. Identify and remediate compliance risks, such as Payment Card Information (PCI), Personally Identifiable Information (PII), financial and other types of data.

Get a Free Shadow Data Risk Assessment for Dropbox

Dropbox

Dropbox Solution Brief

Find and monitor all cloud applications used in your organization and highlight any risk and compliance issues these may pose.

Read the Solution Brief

Elastica Securlet for Dropbox

Detect Threats in Dropbox Accounts

Elastica’s Securlet for Dropbox uses advanced data science and machine learning to analyze user activity and identify risks to sensitive corporate data. A ThreatScore™ is automatically assigned which can be used to warn administrators or prevent breaches before they occur.

Elastica Securlet for Dropbox Elastica Securlet for Dropbox
Elastica Securlet for Dropbox

Protect Data in Dropbox Accounts

Create and enforce policies for your Dropbox account that protect against threats to sensitive data and ensure corporate governance. Granular policies can be easily crafted to prevent breaches of customer account data.

Elastica Securlet for Dropbox Elastica Securlet for Dropbox

Investigate User Activity in Dropbox Accounts

Perform targeted investigations for post-incident analysis leveraging a wealth of detailed transactional information to analyze compliance violations. Powerful visualization, free-form search and extensive filtering criteria provide quick access to needed information.

Learn more about Enabling Dropbox for Business

  • Understand how Dropbox is being used by your users
  • Identify sensitive content (like payment information, healthcare records, source code, or other types of data) being shared
  • Uncover risky or anomalous behavior by rogue insiders
  • Automate protection against Dropbox data breaches, minimize false positives, and eliminate the constant retuning of data classification policies
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