Data Classification: Definition, Guidelines, and Best Practices

Today, every business sector is increasingly data-driven. That makes it more imperative than ever to identify, manage, and protect your data.

Data classification takes an information-driven approach to identify and sort your data. It helps you keep your data secure and in compliance. It also makes your processes and decision-making more efficient. It reduces data management costs and helps your organization reach business goals faster.

What Is Data Classification?

Data classification uses intelligent search to analyze structured and unstructured data and organize it into categories. Categories derived from content, file type, or other predefined criteria.

The purpose of data classification is to understand the relative importance of all your data. It’s vital to identify which data is most sensitive and valuable, and where you are keeping it. Once you know that, you can make informed decisions about how to protect it properly.

Classifying data according to its sensitivity helps you discover whether that data presents a risk if exposed. It also helps you define the consequences in case of a breach. That helps your organization put controls in place to keep sensitive data secure.

Data classification also assists with maintaining compliance with relevant regulatory mandates. For example, GDPR, HIPAA, CCPA, or PCI DSS. It is an essential element of your organization’s information security and compliance program.

It helps conserve resources by focusing your security efforts on the data that most needs protection. Low-sensitivity data doesn’t need a high level of security. That helps you avoid expending valuable resources protecting it.

Your organization needs to know which information requires the greatest measures of protection. Prioritizing risk mitigation helps you protect your data and follow privacy laws. Without data classification, the process becomes much more difficult, if not impossible.

That’s why the importance of data classification grows with the volume of data your organization stores. Not just for the data you store today, but for the data you will store in the future.

The Benefits of Data Classification

Data classification uses intelligent search to help you better understand your data. It scans your data to help you understand what types of data you store and where it is located. This offers your organization several distinct advantages.

First, it gives you important insights into your data to help you mitigate risk. It tells you where you are storing sensitive and regulated data. Once you know which classes of data need protection, you can establish a more effective data security strategy.

It also simplifies the process of managing ever-increasing volumes of data. That boosts user productivity and improves decision-making. It improves your data governance efforts, which also benefits your regulatory compliance efforts.

Data classification also helps identify duplicate, outdated, or unneeded data. Removing that data can reduce storage and maintenance costs. That helps your organization operate more efficiently.

Data classification helps protect valuable data against threats.

Sensitive data is always at risk of a potential data breach. Before you can calculate that risk, you must know the extent of your potential exposure.

Effective data security begins with identifying the kinds of data your business is storing. Data classification tells you what you are storing and where you are storing it.

Once you can pinpoint which portions of data are most sensitive, you know what needs to be protected. You can develop data access guidelines for each category, and allocate resources for protection. Data classification helps you prioritize your security measures and build a stronger defense.

Data classification makes it easier to manage increasing volumes of data.

Automated data classification is indispensable for managing high volumes of data. The process streamlines search functions and discovery to boost user productivity in two ways: first, by helping you identify and remove outdated or redundant data and second, by telling you which data is most often utilized.

Knowing this, you can make informed decisions to help manage your data faster and more effectively than before. This is true even as your data volumes increase.

For example, you can speed up access to high-demand data by moving it to a cloud-based infrastructure or to faster devices. That affords users better access to data based on their needs.

Over time, the amount of data your organization must manage will only increase. Your organization needs to have the tools in place to manage this growing volume.

Data classification improves your governance efforts.

Unfortunately, there is no “one-size-fits-all” approach to your data. The right approach depends on your organization’s particular needs. Your regulatory responsibilities and the unique composition of your data are important factors.

Data classification gives you the information you need to manage your data. Not only when it is first created, but also while it is being accessed, manipulated, stored, and deleted.

It helps ensure your data is accurate. It also facilitates setting up the infrastructure you need. It informs your risk management, legal discovery, and regulatory compliance processes.

Data classification provides you with invaluable business insights.

Bringing order to your sensitive and important data helps you gain valuable insights. It can highlight the data that is most relevant to your business interests. It can identify duplicate and outdated data, minimizing maintenance and storage costs. That helps you achieve more of your business goals.

How to Avoid Common Pitfalls in Data Classification Initiatives.

Success with data classification depends on following best practices. Implementing and executing a data classification project offers tremendous advantages. But it’s important to avoid common pitfalls.

Pitfall #1: Starting a data classification project without a plan.

Define your goals, objectives, and strategic intent before you begin the data discovery process. Also, identify any privacy laws or compliance regulations that apply to your organization. It’s important to incorporate those into your plan from the beginning.

Pitfall #2: Trying to accomplish all the data classification at once.

The right automated data search tools can quickly process large volumes of data. But it’s important to begin any data classification project with realistic expectations.

Create a policy document that details the scope of your program. That creates guardrails to help keep the project on track. Later, you can scale up the project in stages.

Pitfall #3: Creating complex classification schemes.

The most common problem is also the easiest to avoid. Having too many categories of data can add too much complexity to your project. It’s usually best to start with three categories:

• Restricted or High-Sensitivity Data. This data could cause significant damage if compromised by a breach. It is often protected by privacy laws. Examples include intellectual property or financial records. This type of confidential data should have strict access controls and protections. It should be the focus of your security efforts.
• Private or Medium-Sensitivity Data. This data is for internal use and should be accessible only by members of your organization. If exposed in a data breach, the effect would not be disastrous. For this reason, it doesn’t need the highest level of security. Examples include internal process documents, systems information, or internal emails containing no confidential data.
• Public or Low-Sensitivity Data. This data has no access restrictions. It includes customer-facing and media-facing assets. Examples include public website content, marketing materials, or organization charts. This type of data does not need any security protections.

You may need to create more categories for your data. The exact number depends on your organization’s needs.

Remember that each extra category increases the complexity of your efforts. More is not necessarily better. Consider how many categories you need before adding more.

How Data Classification Helps You Meet Compliance Standards

Different compliance regulations have varying requirements about protecting specific data. For example, PCI DSS requires the protection of cardholder information. GDPR regulates the personal data of EU residents.

Your organization must follow a specific combination of regulatory and industry-specific mandates. This often creates a complicated landscape of data attributes that need protection. The solution is data classification.

Data classification helps your organization stay within compliance standards and pass audits. Identifying and classifying information facilitates putting controls in place. That proves to auditors that your data is properly governed, so you can pass audits.

General Data Protection Regulation (GDPR)

Your organization must be able to retrieve complete data sets about an individual. In addition, you must satisfy data access requests within the required timeframe. Data classification helps you find all information on time to meet compliance requirements.

Health Insurance Portability and Accountability Act (HIPAA)

HIPAA compliance requires the ability to accurately inventory ePHI. Also, determine any potential security risks to its confidentiality, availability, and integrity. Data classification enables you to pinpoint where all health records are stored. It helps you implement the security controls necessary to protect this sensitive data.

ISO/IEC 27001

Data that falls under the security standards of ISO/IEC 27001 must have industry standard protections. With data classification, you can organize data by value and sensitivity. That’s crucial to preventing unauthorized disclosure or modification of the data.

NIST Special Publication (SP) 800-53

To meet compliance standards, your organization must ensure data confidentiality, integrity, and availability. That includes identifying everything necessary to maintain systems, applications, and integrations. Data classification helps federal agencies architect and manage their IT systems as required.

Payment Card Industry Data Security Standard (PCI DSS).

Meeting these compliance standards involves mitigating the risk of unauthorized disclosure and access. Data classification simplifies the process of identifying consumer information and keeping it secure.