What are the best practices for securing data at rest?

2 Manage encryption keys

Encryption keys are the secrets that enable encryption and decryption of data, so managing them is essential for securing data at rest. Data governance professionals should follow key management best practices, such as storing keys separately from data in secure storage systems, rotating and revoking keys regularly, implementing access control policies and audit logs, and backing up keys with a recovery plan. Failing to do so can result in data loss or exposure.

3 Implement data masking

Data masking is a technique of replacing sensitive data with fictitious or anonymized data that retains the original format and structure. It can be used to protect data at rest from unauthorized disclosure, while still allowing it to be usable and functional. Data governance professionals should use the right data masking tools and methods for the specific data type and use case, such as static data masking, dynamic data masking, and format-preserving encryption. The former applies masking to a copy of the data before it is moved or accessed, while the latter applies masking on the fly as the data is requested or queried. The latter also encrypts the data with a key that preserves its original format and length.

4 Apply data classification

Data classification is the process of categorizing data based on its value, sensitivity, and risk. This can help data governance professionals identify and prioritize the data that needs to be secured at rest, as well as the appropriate security measures and controls for each data category. Additionally, data classification can facilitate compliance with data protection regulations and standards, such as GDPR, HIPAA, or PCI-DSS. To ensure successful data classification, data governance professionals should use frameworks and tools that support their business objectives and data policies. For example, they can label data with metadata tags that indicate its classification level and security requirements; scan for sensitive or personal information and apply automatic classification rules; and educate users and stakeholders about the data classification scheme and their roles and responsibilities.

5 Monitor and audit data at rest

Monitoring and auditing data at rest is the process of tracking and reviewing activities and events related to the data, such as access, modification, deletion, or movement. This helps data governance professionals detect and respond to any anomalies, breaches, or violations of data security policies and standards. It also provides evidence and accountability for data security compliance and audits. Data governance professionals should use monitoring and auditing tools and techniques that enable them to collect and analyze data logs and metrics from various sources, such as encryption systems, key management systems, data masking tools, and data storage systems. Alerts and notifications should be set up for any suspicious or abnormal data activity or event. Reports and dashboards should be generated to summarize and visualize the data security status and performance.