Partner POV | How Data Minimization Can Improve Cybersecurity, Compliance, and Reduce Cost

This article was written and contributed by our partner, BigID.

In today's digital age, data is one of the most valuable assets a company possesses. Data is critical for business and innovation, it's growing at an exponential rate, it's one of the most challenging threat vectors to address.

Protecting this data has become increasingly challenging: there's a growing number of cyber threats, the sheer amount of data that is being collected and stored is increasing, and cloud adoption has accelerated this challenge. An organization's data is a goldmine for cybercriminals - whether that data is customer data, financial data, regulated data, or intellectual property.

The more data a company has, the more attractive it is to hackers - and if a breach does occur, the damage can be catastrophic. Not to mention, storing too much data can also make it difficult to comply with data protection and privacy regulations.

One of the biggest challenges organizations face when it comes to implementing data minimization is determining what data is necessary to keep and what can (or should) be disposed of. With the vast amount of data generated and collected every day, it can be overwhelming to know what data you have in the first place, what's important (or critical, or sensitive, or regulated) and what data can - or should- be discarded.

By reducing the amount of data stored, organizations can decrease their risk of data breaches and improve regulatory compliance. Data minimization can also streamline data management processes, leading to increased efficiency and cost savings.

Where to Start with Data Minimization

So, how does one begin the process of data minimization? It all starts with knowing your data. Organizations need to have a clear understanding of what data they are collecting, how sensitive it is, and how it is being used. This can help identify unnecessary data - often called redundant, obsolete, or trivial (ROT) data - that can be safely disposed of.

Step 1: Know Your Data

Identify, classify, and tag data by sensitivity, regulation, risk, and context. By inventorying your data, organizations can uncover dark data, identify duplicate data, and prioritize high risk data.

Organizations need to be able to:

• Find and inventory data of all types, regardless of if it lives in data centers or the cloud
• Uncover dark, hidden, or unknown data that you didn't know you had
• Identify duplicate and redundant data
• Flag, tag, and label data by type, meaning, and sensitivity
• Apply data retention and business policies based on the data itself
• Drill down and explore their data to understand what's regulated, what's at risk, and what data can be minimized

Solutions like BigID can automatically discover, classify, and inventory all of your data, everywhere: giving you visibility on the data you know, and the data you don't. Granular classification and categorization makes it easy to understand what data you want to keep, what you can get rid of, and what data is vulnerable and at risk.

Step 2: Manage & De-Risk Your Data

Once you know your data, organizations should assess the risk of that data - improving their data security posture management, and understanding what data poses risk, and where the opportunity is to minimize that risk.

Redundant, trivial, and obsolete (ROT) data only amplifies risk, while there's additional risk in your data based on what it is, if it's regulated, if it contains sensitive data, where it lives, and who can access it. In order to manage and de-risk your data, organizations need to be able to understand the dimensions and context of the data, and be able to quantify, manage, and articulate the risk of it.
 

Organizations need to be able to:

• Manage data by policy and type: secrets and keys in dev environments, for instance, represent one type of data risk; while PCI data (credit card data) may need to be handled differently.
• Identify toxic combinations of data: Find any combination of risky data in documents and structured data sets.
• Report & map data risk by type, sensitivity, and policy - it's important to articulate, report, and audit the findings.
• Investigate data risk to determine what's critical and enable security teams to improve their security posture

Leverage hundreds of policies out of the box - and hundreds of thousands of retention policies out of the box with BigID to get a jumpstart on managing data by policy and type. Easily report on and map data risk, and get accurate results to pinpoint next steps to de-risk your data.
 

Step 3: Take action and minimize your data

Now that you've got the scope of your data landscape under control, it's time to take action and actually minimize your data footprint. Data lifecycle management is a key process here: understanding what data retention rules apply to what data, being able to remediate that data, and consistently reporting on progress.

Organizations need to be able to:

• Manage and implement data retention policies for the data: prioritize policy violations, automate workflows, and take action
• Remediate and assign the right action to the data owner - whether that's deleting, quarantining, or tombstoning data
• Delete the data that's no longer necessary: including duplicate data, high risk data that you no longer need, and more.

BigID enables customers to manage data retention and operationalize data minimization across all of their data to reduce risk, achieve compliance, and automate data lifecycle management:

• Manage retention policies, findings, and violations are managed in one interface
• Leverage out of the box retention policies, or build your own, to manage what data to keep, for how long, and what to delete
• Apply data retention policies consistently across structured and unstructured data
• Remediate the way you want: delegate remediation, move data based on policy, delete data directly, and minimize your attack surface.

Data minimization is an ongoing process, not a one-time event. By understanding the risks of storing too much data alongside the benefits of data minimization, organizations can take proactive steps to improve cybersecurity, regulatory compliance, and overall efficiency.