Data Lifecycle Management - A Quick Read

Data lifecycle management (DLM) is the process of managing data from its initial creation and use to its final disposition. It includes the management of data throughout its lifecycle, from data creation to data destruction. A DLM framework includes several tools and techniques for managing data throughout its lifecycle. Here is a basic framework for DLM, including tools for data classification, security controls for data sharing, monitoring data being shared through emails or cloud storage and tools for data deletion.

1.             Data Classification: The first step in DLM is to classify the data based on its sensitivity and value. Use data discovery and classification tools to classify data based on sensitivity, ownership, and compliance requirements. Subsequently organizations can tag data with metadata such as classification labels and retention policies. These can be done using various tools such as:

• Data Discovery Tools: These tools help to discover sensitive data across various sources.
• Data Classification Tools: These tools automatically classify data based on predefined rules and policies.

 2.             Security Controls for Data Sharing: Once the data is classified, security controls can be implemented to protect the data when it is shared. Some tools that can be used for security controls are:

• Access Control: This tool restricts access to sensitive data by creating user permissions. Organizations can use data protection tools to enforce access controls and encryption policies. Data governance tools can be considered to track data lineage, enforce policies, and ensure compliance.
• Encryption: These tools encrypt data when it is shared, making it unreadable to unauthorized users. Unencrypting is based on access control.
• Digital Rights Management (DRM): This tool restricts the use and distribution of digital content.

 3.             Tools for Data Deletion: This is the area that is never taken seriously in any organization. Data that is no longer needed or is outdated should be deleted to reduce storage costs and to minimize the risk of a data breach. If not deleted, at least the old data should be archived to reduce storage costs. Some tools that can be used for data deletion are:

• Data Deletion Tools: These tools can delete data that is no longer needed, such as temporary files or backups. Scripts to manually identify older data can also be written, however, these are difficult to manage in large organizations.
• Data Sanitization Tools: These tools can remove all traces of data from a storage device, making it unrecoverable.
• File Shredders: These tools overwrite the data with random characters, making it unreadable and irretrievable.
• Hard Disk Destruction: Degaussing is considered very formidable, but nothing can beat the good old hammer to physically destroying the hard disk.

 4.             Monitoring Data Being Shared: To ensure that data is not being shared improperly, organizations can monitor the data being shared using various tools. These tools include:

• Data Loss Prevention (DLP) Tools: These tools can prevent sensitive data from being transmitted outside of the organization.
• Email Security Tools: These tools scan emails for sensitive data and prevent it from being transmitted outside the organization.
• Cloud Storage Security Tools: These tools monitor cloud storage activity and alert administrators of any suspicious activity.
• Network Traffic Analysis (NTA) Tools: These tools monitor network traffic and can detect any unauthorized data transfers.

 Overall, the framework for data lifecycle management includes a comprehensive approach to managing data from creation to deletion, including data classification, security controls for sharing, tools for data deletion, and monitoring data sharing through emails and cloud storage. In summary, a DLM framework includes tools for each of these steps, from creation to disposal. By using the best in line tools and best practices, organizations can ensure that their data is secure and compliant with regulatory requirements. And most importantly, the visibility that this framework provides on the asset base and data flows, prepares the organizations for any breach.