Security for Critical Data

When organization is migrating to digital transformation, data security is a big concern. Digital transformation impacts every aspect of business operation and execution. The volume of data that any organization creates, manipulates, and stores digitally is growing, and that drives a greater need for data governance. Large volume of data security is the biggest challenge for any organization for their entire data lifecycle. 

Data security is a process to protect sensitive data from unauthorized access, corruption, or theft  during the entire data lifecycle.

Here are a few steps to mitigate data risk and implement data security.

• Event Monitoring
• Data Detection
• Data Encryption
• Data Audit Trail

Event Monitoring – This activity includes Prevention, mitigation, and monitoring threats to sensitive data.

• Monitor user activity – Know who is accessing data from where with real-time event streaming and min 3-6 months of event history.
• Prevent and mitigate threats – Define and  build Transaction Security policies using declarative conditions or code to prevent and mitigate threats.
• Drive adoption and performance – Analyze user behavior to enable security training for organization and find security bottlenecks to improve user experience.
• Event Log Files – Create event log file for rich visibility into your org for security, adoption and performance purposes

Data Detection –  Find and classify the sensitive data quickly and mitigate data risk. 

• Monitor Data Classification Coverage – Determine which data in your organization have been categorized versus uncategorized. High sensitive data needs to be secure properly. Label data appropriately to manage data security.

Data Encryption – Encrypt sensitive data at rest while preserving business functionality.

• Encrypt data and maintain functionality – Protect data and attachments while data search, lookups, transportation and storage.
• Key Management – Data encryption key management is very important to secure organization data. It includes control and authorization of data encryption keys.
• Policy Management – Data policy management is defining and managing rules or procedures for accessing data. It allows individuals to follow certain processes to access data during storing or transit.. 

Data Audit Trail – It allows strengthening data integrity for an extended period. This strengthening data integrity process enables compliance and gains insights.

• Data History – Store data as long as you can use this historical data for audit Trail or delete if you do not need this data.
• Data retention policy – Data retention policy defines what data or how long this historical data needs to be stored for audit. Based on sensitivity of data you can archive from 3-6 months or more.
• Insight of data – Create insight and dashboard for data audit transparency. It allows any organization to track any compliance or data security issue.