Each user collects up to 2.5 quintillion bytes worth of data everyday, but for business intelligence, this data can only prove useful if organizations are made aware of exactly where it's stored and if those destinations are protected from unauthorized access. However, often times, in a dynamic network, this information is sprawled across many different computers and their software applications. An effective measure to streamline data and safeguard sensitive items from potential threats is data containerization.
Data containerization involves applications within the same endpoint device, that act as unique and isolated 'containers' for data. With respect to data loss prevention, trusted applications that are used often within an organization to handle critical data can be delegated as data containers for sensitive information by labeling them enterprise-friendly. Then, instead of securing an entire computer which can be tedious, just the enterprise applications can be protected from unauthorized access.
Data stored within endpoint devices can be of varying tiers of sensitivity such that while some data is utilized for informal purposes, other files can include critical information. In the interest of efficiency, non-sensitive information can be left as is with minimal risk, while the security of sensitive files can be prioritized by encapsulating them within data containers for optimal protection.
For organizations that foster a BYOD (bring your own device) environment or allow corporate devices for personal use, security policies can't always be implemented for the entire endpoint without inconveniencing the user. Therefore, it is practical to confine just business-related content to data containers.
If sensitive data is scattered across multiple locations, it can stay hidden and any attempts to exploit or misuse it can go unnoticed. By having data containers and designated spaces for archival of sensitive data, it can be astutely monitored.
Universal rules can be established as to how all data stored in containers should be handled. When work applications are configured to act as designated data containers for sensitive files, employees can be blocked from transferring important from work applications into non-work spaces.
Data within containers can be automatically labelled as sensitive by default which can make it easier to classify further or streamline into automated data classification systems.
Often data disclosure is caused due to negligence and by people who have legitimate access to their devices. In these scenarios, security measures for the entire device such as password protection will not always be the most fitting. With data containers, even authorized users will be blocked from accidentally transferring critical information from work applications to personal spaces.
Due to restrictions on sharing data from enterprise to non-enterprise apps, users may attempt to bypass this measure by using clipboard tools to take screenshots of content. Endpoint DLP Plus enables admins to prohibit these programs to take or share images of sensitive files.
With Endpoint DLP Plus' data containerization capability, admins can classify applications found in their endpoints as 'enterprise apps' or not. It is recommended that any application that is trusted and used to process company information is tagged as enterprise friendly. Once this strategy is implemented, all data that is archived in or originates from enterprise apps will be marked as sensitive and users will automatically be prohibited from transferring data from a enterprise application to an unprotected workspace.
Data containerization with Endpoint DLP can be performed in just a few clicks and is a scalable security measures for organizations of all sizes. Download a 30-day free trial!