Written by:
Anne Gotay So many data security solutions exist in the marketplace today – each designed to protect sensitive data in different ways – making it nearly impossible for data scientists or IT professionals to know which data protection solutions are the best fit for their needs. In this post, we’ll look at three of the more common data security technologies and how they apply to various use cases.
1. Data Encryption Solutions
What it is
Data encryption is the process of using an encryption key to alter data to render it unreadable to anyone who does not possess the encryption key or who is not an authorized user. The original, sensitive data or plaintext is encrypted via sophisticated algorithms that convert it to unreadable text or ciphertext.
When to use it
Data encryption solutions traditionally have been used to protect data at rest or data-in-motion. However, new data encryption technology – known as data-in-use encryption, also protects data while it is being used or queried. Data encryption solutions have multiple use cases, starting with decreasing the impact of a possible ransomware attack, allowing data analysts both internally as well as with third parties to analyze encrypted data without compromising security, as well as protecting from data theft and data breach.
Until recently, organizations frequently avoided data encryption solutions as they were difficult and time-consuming to deploy, and they did not allow for encrypted data to be queried or analyzed. But data-in-use encryption removes these pain points by requiring no changes to application; by having next to no latency; and by not requiring a team of experts to manage the solution. In addition, with data-in-use encryption, encrypted data can be queried or analyzed without the need to decrypt, and they enable organizations to encrypt and manage data in multiple data stores from a centralized platform.
To learn more about data-in-use encryption, here’s a whitepaper that takes a deep dive into in-use encryption, what it is and how it works.
2. Tokenization
What it is
Originally, tokenization was launched to protect payment card data to help retailers reduce their obligations under PCI-DSS. Tokenization converts a data placeholder to a token placeholder, replacing sensitive elements with randomly generated data mapped one-to-one within the environment. The original information is no longer contained within the tokenized version; therefore, the token cannot be easily reversed back to the original sensitive data.
When to use it
Tokens can be used in applications to replace highly sensitive data. When the original data must be retrieved, the token is submitted to the vault. From there, the index is used to fetch the real value for use in the authorization process.
3. Data Masking
What it is
Data masking is frequently referred to as data anonymization. Data masking replaces original, sensitive data by using fictitious data or characters. Several types of data masking exist using various masking techniques such as static data masking (SDM) and dynamic data masking (DDM).
When to use it
The use case for masking data is usually tied to compliance requirements, specifically for a rather quick solution for organizations that must adhere to GDPR, CCPA, or ITAR regulations. A secondary use case is for a test environment with identical data when original data is not needed to test the database environment.