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Artificial Intelligence in Data Privacy and Protection can increase customer trust

The development of Artificial Intelligence and Data Protection domains are largely dependent on the economic and societal needs. While Artificial Intelligence develops better customer services by wrangling trillions of BigData and learning from it, data protection is poised to build trust in people to share data with Organizations.

A recent survey from Gartner showed that over 40% of privacy compliance technology will rely on AI by 2023

Privacy leaders are augmenting technology and specifically data capabilities to ensure that personal data processing is brought into a controlled environment. This can be both costly and time-consuming for privacy functions. Alignment of Data Privacy to Data office will supplement the most up-front costs of procuring such capabilities.

The crux of managing privacy is having to identify the type of data that exists in thousands of databases. This data discovery can now be automated with AI. Further labeling can be performed by data stewards when a certain threshold is not reached for labeling a data element like a national identifier. The strategy used can be data model-column name matching, data matching and matching based frequency of reference values or pattern matching. A data catalog is a Key enabler in data classification.

Restricted Processing is possible through the discovery of data & processing rules, as AI analyzes vast data & Graph connects them. As you know where data exists, the next aspect is to discover in which processes it is being used and how is it being applied. These derivations, transformations are mostly embedded as code, or ETL and need to be managed centrally to restrict processing.

 

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AI in Data Quality is making it easier to detect context-based exceptions. Moreover, AI now makes it possible to automatically discover rules from profiling outcomes while also managing dimensions like Accuracy and Relevancy.

MDM & ML makes it possible to master trust-worthy single copy of data, With multiples copies of master data of customers that are mostly private replicated across the databases - it is difficult to choose a trustworthy, and recent copy. MDM using Graph, and AI learns to master data to in-fact generate insights on householding and further insights that can drive services better.

Bots can now be a Concierge servicing customers by pointing them to information on privacy. Whenever customer requests for their data, there is authentication of the customer as well as information pull-up that is required. Specifying an authorized servicing point like a Bot that can work on these activities in the back-end will reduce efforts from data privacy function.

AI & Graph can be applied to security applications including spam filters, network intrusion detection & authentication. As security applications generate vast amounts of data or as they have access to trillions of logs, AI can help these applications generate accurate insights even before a breach can happen.

 

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