How to Properly Classify Your Data in 2022

Data classification can seem like an overwhelming task, especially for organizations without a solid practice in place. As with any security approach, data classification is both crucial and tempting to avoid. Whether the value is recognized or not, there is a chance that it will be pushed further and further down the priority list in favor of more easily processed items.

In this article, we’ll help you build a case for data classification and fill in some important knowledge gaps to ensure your approach is comprehensive. This will require an investment of resources – time, money and people, in particular – but will, in the long run, help organizations avoid costly mistakes.

What is data classification and why is it important?

Data is the lifeblood of a modern organization. Your data is essential to the development of your business, whatever your sector of activity or your offer. As such, ensuring that your data is secure and easily accessible to the right people is paramount.

At a basic level, data classification refers to organizing your data into categories to make it easier to access, use, leverage, and secure in an efficient way. Proper classification makes it easier to locate and retrieve your data when needed. It is particularly relevant for risk management, compliance and data security.

Data classification is based on categorization best practices using visual labels and metadata tied to predefined criteria. Of course, you can’t classify what you don’t know. To start, you’ll need to focus on data discovery to assess reach. Data lives in many places in today’s modern world, and it’s just as important. Make sure you’re looking at the endpoint, in databases, on network shares, and in the cloud.

Why is data classification important? Its need is driven 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 massive amounts of data. Not only that, but as cloud adoption and changes in work approaches (including hybrid and remote models) grow rapidly, data classification and protection take priority.

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

In environments dependent on cloud services, data is more available to end users and those who need it. Unfortunately, it 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 .

Malicious actors target data for exploitation, including ransomware attacks. Phishing and ransomware attacks are a lucrative business, with damages expected to reach $20 billion in 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 data classification implementation as their goal.

Data classification methods

When selecting a data classification, it usually comes down to deciding which approach to start with. Each method provides insight into organizational data and can be combined to increase security and mitigate the risk of misclassification, whether unintentional or malicious.

Content-Based Classification inspects and interprets file data for sensitive information. This method includes regular expression and fingerprinting, answering the question “What’s in this document?” »

Context-based classification points to apps, locations, creators, or other variables that indicate sensitive information. This approach answers the questions “How is this data used?” », « Who accesses it? “, “Where is this data moved or transferred? and “When is the data accessed?”

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

Gartner recommends that organizations use a collaborative approach to combine the above methods. Chief Data Officers (CDOs) must collaboratively define and use classification capabilities to identify, label and store all data. A combination of user-driven and automated classification will ensure coverage and reliability.

How to implement data classification in your business

As you can imagine, a successful data classification strategy will affect – and depend on – the people in your organization. Key players include:

DSI & RSSI are responsible for data protection and technical liability. Understanding the sensitive data landscape is crucial for both people.

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

Data creators and end users must be very aware of the need to protect data, including the risks and ramifications of data leaks.

Legal and Compliance the actors are particularly concerned by the risks and must be kept informed of the extent of the sensitive data and the measures to protect them.

Early user involvement will promote organizational success in data classification, especially as it affects the workflow of individuals.

Define and implement your policy

As mentioned above, developing and implementing a data classification policy can initially seem overwhelming. Fortunately, the whole process can be broken down into steps to help you (and your organization) see it as a manageable undertaking. The underlying theme of getting started is: just start. It doesn’t just mean ‘start’, but rather start with a simple approach and build from there.

The Digital Guardian (DG) approach to classification and data protection offers a data-centric plan comprised of a four-step framework:

With the DG Data Protection Plan, organizations can protect their valuable data pool from threats (internal and external) by leveraging built-in onboarding automation while limiting false positives and false negatives .

Combining data discovery and classification, policy and enforcement, Digital Guardian offers a comprehensive approach to data protection based on content, user and context.

Stephanie-ShankAbout the Author: Having spent his career in various functions 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 luckiest: someone who makes a living doing what she loves.

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

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