Thales Blog

Data Lakes Security: What, Why, How?

June 3, 2020

Ashesh Thanawala Ashesh Thanawala | Sales Director – India & SAARC More About This Author >

In today’s digital-first world, data is the new oil. The stream of incoming data has become unending and more businesses are sourcing data to boost their targeting. That’s why data repositories have now gained the status of being prized possessions.

Data Lakes Security: What, Why, How?With the size and demand of these repositories growing exponentially with every passing day, we now have diverse types of repositories available for storing Big Data.

As exabytes of sensitive business data get generated daily across the globe, organisations find it difficult to store this type of data centrally in an organised manner. Until the time they find a sustainable, concrete solution, many organisations opt for storing the raw data in an as-is format in centralised repositories commonly known as ‘Data Lakes’.

Demystifying Data Lakes

These data lakes store relational data like operational databases and data from line-of-business applications, as well as non-relational data from mobile apps, IoT devices, social media, etc. Through crawling, cataloging, and indexing, data lakes give organisations the ability to understand the kind of data that is available at any given point in time.

Data lakes work like the body of water they are named after – natural lakes that contain unfiltered water that can be processed to make it potable. Just as water is a precious resource, the data stored in data lakes is equally invaluable too. That is why safeguarding measures need to be implemented to preserve and conserve these data lakes.

Top Data Protection Challenges of Data Lakes

Just the way exposed water in a lake is vulnerable to contamination (and misuse), the unfiltered sensitive raw data in data lakes faces imminent threats of leaks, breaches and thefts in the absence of strong protective mechanisms.

When it comes to protecting sensitive data in data lakes, ‘unauthorised access’ and ‘data theft’ are widely considered as the top two security challenges.

1. Unauthorised Access

If access to data lakes is unauthorised, the data inside it can be easily accessed by anyone leading to its misuse. If this data includes sensitive personal information, corporate financial data, technical trade secrets, etc., securing it through breach-proof access control systems becomes pivotal to data security.

2. Data Thefts

Even with proper access control mechanisms in place, the possibilities of data thefts cannot be ruled out, especially when it comes to sabotage attempts from company insiders. This is why, as an industry best practice, multiple regulatory bodies across the world mandate the use of encryption technologies for safeguarding sensitive data.

When data lakes are compromised and data leaks lead to ransomware, it only serves to damage the reputation of the brand along with a regulatory compliance failure. This can lead to a loss of trust from the brand’s target customers.

Securing Data Lakes

When it comes to data breaches, the question is not “if”, but “when”. With an ever-increasing volume of sensitive data streaming unimpeded in data lakes, mere perimeter defence systems don’t work as effectively as the last line of defence mechanisms like data encryption.

With its robust portfolio of encryption, centralised key management, and identity management with access controls, Thales offers end-to-end protection for securing sensitive data in data lakes.

Specifically, Thales’s Vormetric Transparent Encryption comprehensively addresses data lake use-cases for encrypting sensitive data at the OS-level. Furthermore, with strongly defined access controls for individual user groups, it efficiently maintains audit and trial logs to meet compliance regulations such as Payment Card Industry Data Security Standard (PCI DSS), General Data Protection Regulation (GDPR) and Reserve Bank of India’s data protection mandates.

For more information, I invite you to read Are You Ready to Take the Plunge? and A Beginner’s guide to Data Lakes.

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