In order to comply with GDPR, companies have to take measures to protect the data they process. But first they have to actually find the data.
Use Cases > Regulations Compliance and Governance > GDPR Compliance Use Case: Locate Privacy Sensitive Data Instantly
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In order to comply with GDPR, companies have to take measures to protect the data they process. But first they have to actually find the data.
GDPR demands that companies processing sensitive personal data of European residents take additional measures to ensure protection of said sensitive data. A large part of this pertains to access – giving people access to their own personal data, enabling portability of the data, changing or deleting the data.
Before any company can allow access to personal data, it must first locate the sensitive data. Let’s take
credit card columns for example.
A Business Analyst in a large insurance company must identify every single place customer credit card
columns reside inside the company’s reporting systems. Once he does so, he will need to either eliminate the credit card number column from the report or mask the data inside the column so as to protect this data’s security and comply with GDPR.
Finding every single location credit card columns exist within a company’s reporting systems is a ton of
work and can take a really long time. The Business Analyst has to consult with multiple different IT
functions in order to understand how to conduct his manual search for the sensitive data required, and
must pay close attention to the impact of any potential change to the column. Not a simple task.
With Octopai, this entire process is automated. Data & Analytics Teams can auto-discover, access and retrieve
metadata in seconds.
The Business Analyst simply has to enter “credit card” into the search field, and even if this is not the exact name of the column (sometimes the column name differs slightly – credit_c or c_credit, for example), Octopai maps out all the reports related to this specific sensitive data, and instantly locates everywhere the credit card columns resided within every single reporting system. He then was able to perform the data masking required in order to comply with GDPR.
Providing unprecedented visibility and trust into the most complex data environments.
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