How to Improve Data Discovery with Sensitive Data Intelligence

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According to reports from IDC and Seagate, data is expected to grow at an exponential rate in the coming years. Reports suggest that by the year 2025, there will be an increase of data by 175 zettabytes. This amount of data can be beneficial to organizations, as it will serve as an asset, but at the same time, this data needs to be organized and secured in order to avoid any non-compliance of global privacy regulations.


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The problem is not the increase in data, but rather the ability to keep track of this rapidly expanding data. Currently, organizations store data in on-premises systems, public clouds, and private clouds, and with third parties. The growth and sprawl create a big challenge for security administrators that are required to protect data from insider threats and external adversaries.

In order to avoid any complications and non-compliance with global privacy laws, organizations need to have a reliable and efficient data discovery framework in place. Here are the steps that organizations can take to set up their data discovery evaluation system.

Data Discovery Evaluation Guide

There is no set rule as to how to get the best possible data evaluation system, but experts believe that following these steps can put you on the path to an almost perfect data discovery system.

1. On-premises and multi-cloud asset discovery

On-premise refers to data that is stored within the company’s storage infrastructure, whereas multi-cloud means the data is stored in cloud servers. Organizations are advised to incorporate automated tracking in order to discover both the on-premises and multi-cloud data assets and consolidate them into a centralized point. The reason experts suggest automation is because manual methods to track large data volumes increase the risks of breach and human error.

2. Discover sensitive and personal data in data assets

Once you have centralized all your data assets, the next step is to catalog the discovered data as per their sensitivity. Creating a catalog for all your sensitive and personal data within your data assets can make it easier to know which data merits additional protection.

Here are some of the steps that need to be taken in order to classify your data catalogs:

a. Organize your data based on an agreed scheme
b. Categorize data according to types, format, regulation, sensitivity, retention
c. Identify data and apply index/tagging
d. Assign risk profiles to data
e. Dispose of/discard data that isn’t needed
f. Build a searchable database or filter to easily look up data attributes
g. Pinpoint where sensitive data is present

You can also run delta scans on your data that include policy-based scanning and selective scanning. Data is constantly being modified and shared, so it is necessary to have a strategy that monitors data stores for new instances of sensitive information.

3. Link data to its correct data owner

Once data has been discovered and classified, the third step is to link it with its correct data owner. This exercise will help you fulfill data subject requests in the future, which is an integral part of any privacy regulation such as the CCPA and GDPR. Organizations should allow users to request modification, update, erase their data, and respond accordingly. This process can be simplified through data linking.

4. Create compliance reports

Under the GDPR and CCPA, along with other privacy regulations, organizations are required to have up-to-date and comprehensive records of their data processing activities. Such records and reports can help organizations to demonstrate compliance with the applicable legal requirements and regulatory bodies to verify it.


Data is evolving, and so are the methods through which this data needs to be handled. Organizations are encouraged to implement automated operations and simplify data discovery using the aforementioned steps. This will help them to ensure compliance with global privacy regulations and respect the data privacy of their customers.

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