How to Correctly Classify Your Data in 2022

Data classification can feel like an overwhelming task, especially for organizations without a strong practice. As with any security approach, data classification is both critical and tempting to avoid. Regardless of whether the value is recognized, there is a chance that it will continue to be pushed further down the priority list in favor of issues that are easier to deal with.

In this article, we’ll help you create arguments for data classification and fill in some key knowledge gaps to ensure your approach is comprehensive. It requires an investment in resources—particularly time, money, and human resources—but helps companies avoid costly mistakes in the long run.

What is data classification and why is it important?

Data is the lifeblood of a modern organization. Your data is critical to the success of your business, regardless of your industry or offering. Therefore, it is of paramount importance to ensure that your data is secure and easily accessible to the right people.

At a basic level, data classification refers to organizing your data into categories for effective ease of access, use, usage, and backup. Proper classification makes it easier to find and retrieve your data when needed. It is particularly relevant for risk management, compliance and data security.

Data classification relies on best practices for categorization using visual and metadata labels in relation to predefined criteria. Of course, you can’t classify what you don’t know. First, you need to focus on data discovery to gauge the scope. Data is present in many places in today’s modern world, and they are all equally important. Make sure you’re looking at the endpoint, at databases, at network shares, and at the cloud.

Why is data classification important? Its need is determined by many factors, including governance, industry-specific regulatory requirements (such as HIPAA, GDPR, PCI, CCPA, and others), compliance, IP protection, or simplifying your security strategy.

Why data classification is fundamental

Organizations generate huge amounts of data. Not only that, as cloud adoption and changes in working approaches (including hybrid and remote models) rapidly increase, data classification and protection are priorities.

Recent reports have found that more than half of organizations have all of their applicable infrastructure in the cloud, and nearly three-quarters of organizations host more than half of their workloads in the cloud. In 2021, cloud adoption increased by 25%, helped by the pandemic and changing ways of working.

In environments that rely on cloud services, data is more available to end users and those who need it. Unfortunately, this also makes the data more vulnerable to security threats. Well-designed data classification is critical to data security and governance, including data loss prevention (DLP), enterprise digital rights management (EDRM), and data access governance.

Criminals target data for exploitation, including ransomware attacks. Phishing and ransomware attacks are a lucrative business, predicted to cost $20 billion in damage by 2022. With numbers like these, it’s clear why organizations and security professionals are investing in data classification. In fact, 72% of security decision makers have their sights set on implementing data classification.

data classification methods

Choosing a data classification is usually a matter of which approach to start with. Each method provides insight into enterprise data and can be combined to increase security and reduce the risk of misclassification, whether accidental or malicious.

Content-Based Classification examines and interprets file data for sensitive information. This method includes regular expressions and fingerprinting and answers the question “What’s in this document?”.

Context-based classification refers to applications, locations, creators, or other variables that indicate confidential information. This approach answers the questions “How is this data used?”, “Who is accessing it?”, “Where is this data moved or transferred to?”, and “When is the data accessed?”

User controlled classification relies on end-user or otherwise manual selection based on the user’s knowledge and discretion at the time of creation, editing, or review to identify sensitive data and documents. This method requires a well-defined workflow.

Gartner recommends organizations use a collaborative approach to combine the above methods. Chief Data Officers (CDOs) should collectively define and use classification capabilities to identify, tag, and store all data. A combination of user-driven and automated classification ensures coverage and reliability.

How to implement data classification in your company

As you can imagine, a successful data classification strategy impacts—and relies on—the people in your organization. Key players include:

CIO & CISO are responsible for data protection and technical responsibility. Understanding the sensitive data landscape is crucial for both individuals.

Business User Guidance Members will understand that data classification increases the visibility and protection of customer and product development data.

Data Creators and End Users should be keenly aware of the need for data protection, including the risks and implications of data leakage.

Law and Compliance Players are particularly concerned about risk and should be kept informed of the volume of sensitive data and measures taken to protect it.

Involving users early on will support organizational success in data classification, especially as it impacts the workflow of individuals.

Define and implement your policy

As mentioned above, developing and implementing a data classification policy can feel overwhelming at first. Fortunately, the entire process can be broken down into steps so that you (and your organization) view it as a manageable endeavor. The overarching theme of the introduction is: just start. It doesn’t just mean “start,” but rather start with a simple approach and build from there.

The Digital Guardian (DG) Data Classification & Protection approach offers a data-centric plan consisting of a four-tiered framework:

Through the DG Data Protection Plan, organizations can protect their valuable pool of data from threats (both internal and external) using built-in automation of integration while limiting false positives and false negatives.

By combining data discovery and classification, policy and enforcement, Digital Guardian offers a comprehensive approach to content-, user- and context-driven data protection.

Stephanie ShankAbout the author: Having spent her career in various roles and industries under the High Tech umbrella, Stephanie Shank is passionate about the trends, challenges, solutions and stories of existing and emerging technologies. A storyteller at heart, she considers herself one of the lucky ones: someone who can make a living doing what she loves.

Editor’s note: The opinions expressed in this and other articles by guest contributors are solely those of the contributor and do not necessarily reflect those of Tripwire, Inc.

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