Data discovery and context-aware data security. With the increasing use of the cloud, remote work and interconnectivity of business processes, sensitive data is now stored in a multitude of systems, applications and databases, which makes protecting it a challenge.
To effectively protect data from loss and theft, a company needs to know not only what data it owns, but also where it is stored, who has access to it, where it is being accessed and how it is being transferred. As one of the biggest business intelligence trends in recent years, data discovery is therefore a crucial component of data security and compliance. Data discovery enables a company's sensitive and legally regulated data to be comprehensively identified and localized in order to secure it appropriately or reliably remove.
Data discovery enables data security
Data discovery makes it easy for organizations to identify data threats and mitigate the consequences of potential data breaches. With a thorough understanding of context factors such as file type, sensitivity, user, and location, security teams and the security solutions they deploy can make more effective decisions when it comes to protecting sensitive data in a wide range of use cases. Data discovery provides much of this contextual indication by identifying sensitive and regulated data.
Advantages: data discovery with context-aware security
Today, corporate data resides on many devices and in cloud storage applications. Employees or partners can often access it from anywhere at any time. Identifying, locating, and classifying this data is therefore a priority for data discovery security applications. The benefits of data discovery and context-aware security solutions include:
- A better understanding of the type of data a company owns, where it is stored, who can access it, and where and how it is transferred
- The application of predefined classifications and protection guidelines to company data
- Continuous, comprehensive monitoring of data access and data activity
- An automatic data classification based on context
- Risk management and regulatory compliance
- Complete data transparency
- The identification, classification and tracking of sensitive data
- The ability to apply real-time protection controls to data based on pre-defined policies and contextual factors
Data Discovery Best Practices
Data discovery can generally be broken down into five steps:
1. Collect data: Both sensitive and non-sensitive data must be collected and easily accessible. To ensure compliance with legal regulations, the location of the information collected should be summarized and documented as much as possible.
2. Data analysis: As soon as all data is in a manageable environment, it is analyzed. It is important to separate the sensitive data and the necessary but non-sensitive data. Companies also determine which data they must keep due to legal regulations or for business purposes and which data can be discarded.
3. Data cleansing: All unnecessary data should be cleaned up. There should be a general policy for cleaning data when it is no longer required.
4. Protect data: All data should then be adequately protected. This protection should be both physical (keeping the data in a locked cabinet or room) and digital (with a firewall, encryption, etc.).
5. Use data: The discovered data can ultimately be used to gain insights into improving business processes and other company processes.
Companies today are creating data at an unprecedented rate. Data Discovery enables companies to adequately assess the complete data picture and implement the appropriate security measures to prevent possible data loss.
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