Data Lifecycle Management: A Comprehensive Approach for IT Security

The average cost of a data breach in 2024 was $4.88 million. That number doesn’t include priceless damage to your reputation and consumer trust. 

Protecting data isn't just about blocking intruders. You can build higher walls, but what happens when someone opens the gate?

There’s another way to approach IT security, known as data lifecycle management. It's a practical framework that protects your data not just at the perimeter but throughout its entire journey through your organization.

What Is Data Lifecycle Management?

Data lifecycle management (DLM) is an approach to managing data from its inception to its destruction. This includes key stages along the way, such as: 

• Processing 
• Storage 
• Usage 
• Archiving
• Deletion 

During its lifecycle, data undergoes various processes and is shared with users with differing roles and permissions.

DLM is much more than simple tracking and managing information. It establishes a system of policies and procedures that govern how data is handled at all times. 

A DLM strategy helps improve data security. It enhances the risk management process by mitigating system breaches and data loss and bolstering compliance.

Data lifecycle management is critical to maintaining the CIA triad of cybersecurity. What is the CIA triad? Confidentiality (keeping sensitive data from unauthorized users), Integrity (maintaining accurate, unmodified data), and Availability (delivering data to the right users at the right time).

Think of data as paper documents in an office. Most businesses see these documents scattered all over the place. Some are on desks. Others may be on the floor or in the trash. In theory, unused documents are stored somewhere out of sight. 

DLM provides a filing system where documents are always stored somewhere traceable and fit for purpose. Everyone knows where the papers or records are and when they will be sent to the shredder. 

The Importance of Managing the Data Lifecycle

The amount of data your business and the rest of the world create is growing exponentially. Statista projects the total amount of data created and consumed globally to grow from 149 zettabytes in 2024 to more than 394 zettabytes by 2028.

These data locomotives look like runaway trains, but modern tools and techniques keep them on track. More data means greater opportunities for those who know how to process, store, and use it.

A key to prospering from Big Data is data protection and privacy compliance. McKinsey reports that 68% of consumers find the protection of email content to be “very important.”

Regulations like GDPR, HIPAA, and CCPA mandate specific data handling practices with severe penalties for non-compliance. 

Many of these compliance regulations are evolving. As your business expands, you may find you need to adhere to more requirements with every passing year. 

Having a DLM strategy enables your organization to maintain compliance with moving goalposts. It guides you to navigate audits that put your process under the microscope.  

Data Lifecycle Management Benefits

Managing data throughout its lifecycle takes diligence and the right tools. Mordor Intelligence estimates that the global data management platform market will grow from $2.5 billion in 2025 to $4.35 billion by 2030. Why such vociferous growth?

 Well, DLM requires a heavy upfront investment but can pay off dividends for your organization. 

Data lifecycle management benefits include:

• Higher quality data: Establish procedures that regularly clean, validate, and verify data. End users get more accurate, reliable, and up-to-date data.
• Greater access and availability: Control data availability efficiently. The right people can always access the right data at the right time.
• Smoother compliance: Monitor, track, and verify data at every stage. No more scrambling during audits. You have complete documentation about where information resides, who has accessed it, and where it's been.
• Improved productivity: Your team spends less time dealing with errors and locating data. Manual tasks are automated, saving time for critical work.
• Better customer service: Sensitive customer data is safely handled and secured, alleviating consumer fears and fostering trust. Customers can access or delete their data quickly when needed.
• Enhanced security: Every byte of information is visible at all times. Combined with cybersecurity tools, you can protect data at every stage. If a company email leaks or another breach occurs, you'll know immediately what was compromised.
• Reduced operational costs: Stop paying to store unnecessary data. Optimize spending with archiving procedures and avoid regulatory fines that can reach millions. Automated policies reduce manual data management costs.

Stages of the Data Lifecycle

The keyword to any DLM framework is lifecycle. If you only manage your data in some aspects but ignore others, you’re still inviting security and compliance risks. To avoid overlooking anything, it's best if you understand the key stages of the data lifecycle.

Creation

Data always comes from somewhere. Creation is the starting point of the information journey. Maybe a salesperson updates your customer relationship management (CRM) system, or a customer places an order. Data is created during:

• Input: manual data entry or automated by AI tools and devices
• Acquisition: third-party data from financial institutions, communications records, and industry publications
• Capture: IoT sensors and monitoring tools that stream data continuously

During its inception, data is classified. Is it sensitive and confidential? Is it more run-of-the-mill information, such as today’s date? Metadata creation is a significant part of the classification and tagging process that determines the destination of each newly created byte.

That means creation management is a crucial aspect of security. Misclassified data is at risk of being exposed and accessed by unauthorized users or worse. 

Processing

Raw data is like crude oil. It’s not very useful until it’s refined. Data processing transforms information into formats that systems, employees, and customers can actually use.

Processing transforms data into stable formats, integrates multiple sources, and validates for accuracy. During each phase, data is temporarily held in different locations with logs recorded. Your DLM framework must manage system credentials and access controls throughout.

Much of this happens behind the scenes through ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) pipelines. These pipelines pull data from multiple sources, clean and restructure it, and then deliver it where needed.

Storage

Once processed, it’s time to find a home for your data. DLM acts as a realtor, finding the right house for each dataset. A common solution to efficient storage is to use a relational database management system (RDBMS). It maintains data integrity and accuracy through regular storage and retrieval.

Other options include NoSQL servers for unstructured data and data lakes for high-volume data storage.

Data storage procedures should include:

• Backups onsite and in the cloud
• Archiving of older data
• Data retention and compliance policies
• Database maintenance standard operating procedures (SOPs)
• Data protection measures, such as encryption, authentication, and access control 

Usage

We’ve finally made it to the fun part—data usage. All that hard work to protect and maintain your data has paid off. Now, you get to use that data to run your business, sell products, and serve customers.

During usage, data is actively accessed, shared, and modified across your organization. Marketing analyzes drip campaigns. Team members send emails. Sales pulls up the finances of a B2B customer. 

Each activity introduces a new risk. Poor security training and lack of oversight can end up with an employee downloading a virus attached to an email or hitting that “reply all” button at the wrong time.

Usage under a DLM framework means:

• Real-time data governance and access monitoring
• Automatic permission reviews
• Clear sharing policies
• Employee security training for handling emails, recognizing phishing attacks, and other precautionary measures
• Complete traceability and audit trails

Archiving

Not all data is useful or relevant at all times. However, you might still need to keep it on file somewhere. Archives store old projects, former client emails, and past compliance reports out of the way, where they are accessible if needed. 

Archiving frees up expensive storage while maintaining compliance with retention requirements. The security trap? Archives become forgotten graveyards of sensitive information. Hackers know organizations rarely monitor old data carefully.

Smart archiving requires:

• Encrypted storage with restricted access
• Regular access reviews
• Clear retention schedules
• Documented retrieval procedures

Poor archive management is a ticking time bomb waiting for hackers to exploit. 

Deletion

All data hits the end of its life at some point. Deletion is a key aspect of data security. If the data no longer exists, how can scammers and malicious actors access it?

Create a process to examine every piece of data before confirming destruction. Do any compliance and retention regulations apply, or has a user requested deletion?

When it is time to end a digital lifecycle, ensure you have secure destruction protocols that account for all backups. You don't want a security breach down the road due to data that was retained by accident. Additionally, every deletion should be documented somewhere for compliance purposes.

DLM Best Practices

Don’t overcomplicate data lifecycle management. Follow the best practices and opt for practicality when possible.

• Establish clear policies and procedures: Identify and understand all of your compliance requirements. Build retention and deletion policies around this regulation. Create data governance and access controls to ensure only the right people have access to sensitive information.
• Use the right tools: Use database tools and data management platforms to centralize and automate processing, storage, archiving, and deletion. Use cybersecurity tools to protect your data during every stage of life. For example, a tool like Guardian Digital provides email security intelligence to manage communication data.
• Maintain high-quality data: Build a process that not only secures but also verifies data for accuracy and validity. This will ensure data is in usable formats, up-to-date, and accessible to authorized users at all times.

DLM is More Than a Security Framework

Data lifecycle management ensures you control every piece of data that touches your organization. With greater visibility, traceability, and the right processes in place, you’ll have enhanced security for handling anything from customer accounts to company emails. You'll also maintain compliance with evolving requirements.

While it’s vital to protect sensitive information, a DLM approach offers numerous additional benefits. Eliminate redundancies and manual processes with data management automation tools. Your team can focus on higher-level tasks.

Use DLM to reduce storage costs and increase data availability. The right people with the right permission can access the data they need when they need it, every time. Rethink your cybersecurity by building a data lifecycle management strategy today, and never look back!