Data Discovery

50 + data source connectors to fetch data into our subsystem for discovery

50 + data source connectors

Our system comes with more than 50 data source connectors to fetch data into our subsystem for discovery against more than 100 classification attributes. The discovery engine uses artificial intelligence apart from regular methods of discovery such as REGEX and LookUp. Each attribute discovery algorithm is continuously tuned to provide more and more accurate detection.

Apart from the box elements, the SecurelyShare platform is built on a fully scalable and pluggable microservices architecture which allows us to add new attribute definition and consuming data from any data source.

Data Classification

Dynamically create & manage classification against any attribute

Data classification is as important as data discovery itself. A data element may be very important for somebody while the same might not be as useful for others. This can be seen in various privacy laws across the globe as well as different policies and process with the organisations itself. For instance, Trade Union Association is considered sensitive/confidential information about an individual in GDPR, while there is no mention of the same in India’s PDP Bill.

Our System allows us to dynamically create and manage classification against any attribute as per the regional/regulatory compliance or business process. It also allows the creation of new classification levels as well for any business use-cases. These new/existing classifications are smoothly tied up to another process of the system to provide better results at any step.

Dynamically create & manage classification
A unified process for building correlation of the data

Data Correlation/Mapping

A unified process for building correlation of the data

The value of data will exponentially increase/decrease just by correlation/mapping of the data. A mobile number for instance is a piece of personal information but more valuable if we are aware that it belongs to an individual.

We at SecurelyShare have built a unified process for building correlation of the data which uses the best of both data lookup approach as well as artificial intelligence. We allow the automation system to start simply by lookup and then use a self-learning capability to provide better correlation. The process helps the system to fine-tune the discovery process at regular intervals along with building a Golden Customer Record. In a typical enterprise, there are multiple data stores maintaining different versions of customer information. A Golden Customer Record is built to solve the stale data problem as well mitigating the privacy risks by reducing the footprint of the customer information at multiple places.

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