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What is Data Lifecycle Management?

Aidan Simister
| Read Time 5 min read| Published On - August 24, 2022

Data Lifecycle Management

Organizations are storing more data than ever before, in more places than ever before. Data is stored on endpoints, on-premise servers, and cloud-based storage containers, and organizations need a way to locate and track all of the sensitive data they are responsible for. This is where Data lifecycle management comes in.

What is Data Lifecycle Management?

Data lifecycle management (DLM) is a policy-based approach to managing the flow of data throughout its lifecycle, including how and when data is collected, how and where it is stored, and how and why it is created, accessed, moved, modified, removed or shared. Adopting a Data lifecycle management approach will help organizations keep their data secure, and comply with the relevant data privacy regulations.

As mentioned, Data lifecycle management is an approach, as opposed to technology, although it would be practically infeasible to adhere to a DLM approach without having the right solutions in place. At the very least you will need a solution that is capable of real-time auditing and data classification. It would also be advisable to have a solution in place which automates the process of storing frequently accessed and critical data on separate, faster, and more expensive storage devices.

The Benefits of Data Lifecycle Management

There are three main benefits to adopting a Data lifecycle management approach, which is as follows:

1. Security and confidentiality

With a Data lifecycle management approach, access to sensitive data is controlled and continuously monitored for suspicious activities. Data is encrypted, both at rest and in transit, which helps to ensure that it is only accessible to unauthorized users.

2. Data integrity

One of the main goals of Data lifecycle management is to ensure that data is reliable and accurate, irrespective of where it is stored, how it is accessed, and whether other copies exist. Data integrity is achieved through data classification, a robust set of access controls, and continuous real-time monitoring of access to privileged accounts and sensitive data.

3. Data availability

As we understand, if data is made too available, we will encounter security risks. Yet, if data is not available enough, we run into usability problems. The Data lifecycle management approach will ensure that access to sensitive data is granted to those who need it to perform their role, and revoked when access is no longer required.

Data Lifecycle Management Framework

Generally speaking, there are five lifecycle phases that data will go through, which are laid out by the Data lifecycle management framework. These phases include creation, storage, usage, archival, and destruction, which are described in more detail below.

Data creation

The first phase relates to the creation and capture of data, which can take the form of Word documents, images, PDFs, SQL database entries, and any information generated by devices, servers, and other network hardware.

Data storage

This phase is about what data is stored, how and where it is stored, and why. It will also include policies for how sensitive data is stored in the cloud, as well as backup and recovery processes.

Data usage

This phase relates to the way data is used, and relies heavily on data classification to ensure that access controls can be applied quickly and effectively. All data usage must be monitored and recorded in a tamper-proof fashion.

Data archival

When data is no longer in use, it will need to be archived. The purpose of archiving data is to minimize the amount of maintenance required to secure it, thus allowing you to focus on the data that is most relevant. This phase will describe the procedures for storing archived data, and the procedures for restoring it, if necessary.

Data destruction

As above, it is generally a good idea to remove any data that is no longer useful to your organization, as it will make it easier to secure the data that is useful. Data destruction is usually carried out after the data has been archived, to be sure that it is no longer in use. Data destruction will need to be carried out in accordance with your DLM policies.

How Can Lepide Help with Data Lifecycle Management

Lepide provides a real-time change auditing and reporting that gives you visibility into what data you store, where it is located, and how and when it is used.

Event data is aggregated from multiple sources, both on-premise and cloud-based, and presented via an intuitive console.

Via this console you can view a summary of changes made to your privileged accounts and sensitive data, and receive real-time alerts on important events. It also allows you to detect and respond to events that match a pre-defined threshold condition, which can help to prevent the spread of ransomware, and other types of malicious activity.

Our data classification tool is provided out-of-the-box and can be configured to meet the requirements of all significant data protection laws, such as GDPR, HIPAA, SOX, FISMA, CCPA, PCI-DSS, and more.

Our solution also provides a wide range of pre-defined compliance reports which can be used to demonstrate your compliance efforts to the relevant authorities.

If you’d like to see how the Lepide can help you develop and maintain an effective Data lifecycle management strategy, schedule a demo with one of our engineers or start your free trial today.

Aidan Simister
Aidan Simister

Having worked in the IT industry for a little over 22 years in various capacities, Aidan is a veteran in the field. Specifically, Aidan knows how to build global teams for security and compliance vendors, often from a standing start. After joining Lepide in 2015, Aidan has helped contribute to the accelerated growth in the US and European markets.

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