What are the best ways to encrypt data throughout its lifecycle?

1 Data encryption at rest

Data encryption at rest refers to applying cryptographic algorithms to data stored in databases, files, disks, or other media. This prevents unauthorized users or attackers from accessing or copying the data without the proper keys or credentials. Data encryption at rest is essential for complying with data privacy regulations, such as GDPR or HIPAA, and for reducing the risk of data breaches or leaks. Some of the best ways to encrypt data at rest are transparent data encryption (TDE), application-level encryption, and hardware-based encryption. TDE automatically encrypts and decrypts data at the database or file system level, while application-level encryption encrypts and decrypts data within the application logic. Hardware-based encryption uses dedicated devices or modules, such as self-encrypting drives (SEDs) or hardware security modules (HSMs). Each of these methods offers its own advantages and disadvantages in terms of performance, security, cost, complexity, and flexibility.

2 Data encryption in transit

Data encryption in transit refers to applying cryptographic algorithms to data moving between different locations, such as servers, clients, networks, or clouds. This is essential for preserving the confidentiality, integrity, and authenticity of data exchanged over the internet or other networks. To ensure data security while in motion, some of the best strategies are using secure protocols such as HTTPS, SSL/TLS, SSH, or VPN; employing end-to-end encryption (E2EE), which encrypts and decrypts data only at the endpoints of communication; and utilizing homomorphic encryption (HE), which encrypts and decrypts data using mathematical functions that preserve its properties and operations. All of these methods help prevent unauthorized users or attackers from intercepting, modifying, or redirecting the data.

3 Data encryption in use

Data encryption in use is the application of cryptographic algorithms to data being processed or manipulated by applications or services, such as analytics, machine learning, or cloud computing. This prevents unauthorized users or attackers from accessing or exploiting the data while it is in memory or in execution. Achieving the balance between security and performance is challenging as encryption can slow down or limit the functionality of the applications or services. Trusted execution environments (TEEs) are one solution as they isolate and protect the data and code from the rest of the system, using hardware or software mechanisms, like secure enclaves, hypervisors, or containers. Multi-party computation (MPC) and differential privacy (DP) are also useful for encrypting data in use. MPC distributes and encrypts the data among multiple parties while DP adds noise or randomness to the data or results, limiting the information that can be inferred from it. This ensures collaborative and privacy-preserving analysis without relying on a third party or central server.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?