How to Keep Compliant With Data Classification

How to Keep Compliant With Data Classification

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Data compliance is a task that all companies face in light of increasing privacy regulations. For firms in the financial, medical and other more heavily regulated industries, though, the burden of managing data in specific and narrowly prescribed ways is even more pressing.

Privacy regulations now exist in many countries where organizations frequently conduct business. One of the regulations that sent shockwaves through the corporate world and required new data habits was Europe’s General Data Protection Regulation (GDPR), which puts strict rules on how data should be collected and maintained for any organization engaging with a European Union citizen. But other privacy regulations also encompass the typical business, including the restrictive California Consumer Protection Act (CCPA) and Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA).

The compliance burden gets larger for firms in regulated industries. Financial firms, for instance, must contend with data compliance rules from the Gramm-Leach-Bliley Act (GLBA), Sarbanes-Oxley (SOX), New York State’s cybsersecurity rules for financial firms, 23 NYCRR Part 500, industry-led regulations such as the Payment Card Industry Data Security Standard (PCI-DSS), and other laws depending on the firm’s specific jurisdiction and target markets.

Maintaining compliance in light of this vast and evolving data regulation landscape requires strong planning, security and process. Essential for this compliance is a comprehensive data classification regime.

Understanding Data Classification

The concept is simple. The applications of rules and protections for regulated data require understanding the nature of each piece of data and how it fits into regulatory and sensitivity categories so the correct processes can govern the data. An internal spec sheet for a company product might require basic security and simple access controls, while financial data or personally identifying information (PII) will have additional access restrictions, governance rules and processes that ensure compliance with applicable regulations.

Data classification is a process of defining the various groupings that company data might fall into, including level of sensitivity, the various regulatory rules that might encompass it, and the needed security practices around each type of data. Organizational data then is classified individually according to its groupings, and from this classification appropriate automation can ensure that regulatory, security and process requirements are met in full.

Classification is essential for compliance, but it also serves additional benefits for most companies. Other applications for data classification include risk mitigation from properly identifying intellectual property and other sensitive corporate data resources, efficiency optimization through better handling of data as a result of comprehensive data tagging, and improved analytics opportunities since visibility and fine-grained data categorization enable more precise analysis.

Implementing Data Classification for Compliance

Most businesses have some form of data governance and classification already in place, especially those in regulated industries. The trouble is that most data classification regimes do not have comprehensive data classification, which opens the door for data leakage and compliance violations. More than 50 percent of all corporate data goes unclassified or untagged, according to recent research.

Developing a data classification scheme that is comprehensive and meets all data compliance obligations requires a planning and implementation process that is developed from the ground up so no regulated data slips through the cracks.

There are seven key steps for creating this comprehensive classification regime.

Step 1: Define Classification Objectives
What is the company looking for, and why? What regulations apply to the company? Which systems are in scope for the initial classification process? Is classification intended to achieve additional objectives, or just improved data security?

Step 2: Categorize Data Types
What kinds of data are created and exist within the business? Which data is proprietary, and which is public? What is the regulated data?

Step 3: Define Classification Levels
How many levels of classification are necessary? What are examples of data and documents at each level?

Step 4: Establish an Automated Classification Process
How will classification automation take place? What is the process for defining what data will be scanned first? What frequency and resources will be used to automate classification?

Step 5: Specify Classification Criteria and Review
What process of review and validation will be used for checking the automated classification process? What classification patterns and labels within the automated classification solution will be used to achieve the correct data classifications?

Step 6: Define Overall Outcomes and Classified Data Usage
What analytics processes will be used on the classification results? What are the expected outcomes from the analytics analysis? What compliance and risk mitigation steps and automated policies will be put in place for various classifications?

Step 7: Monitor and Maintain Classification
What ongoing process for classifying new and modified data will be used? How will the classification process be reviewed, and with what frequency? How will the company monitor changing business needs and regulations for ongoing classification relevancy?

A key element in this data classification process is full visibility for classification. While many businesses map out a data classification scheme, unclassified data often occurs because of a failure with the final step of monitoring and maintaining classification. Many organizations simply fail to have the right technology in place to maintain adequate visibility for ongoing classification.

If your organization is among those that lack full real-time visibility of all corporate data across devices and geographies, including data created in the home during this season of increased remote work, schedule a demo to learn more about Qohash’s Qostodian data discovery and classification platform. Qostodian enables quick and precise discovery and classification of all corporate data resources. Crucially, beyond initial data classification the cloud-based platform also uncovers and classifies new data as it is created.

We’ve developed a free guide that goes into more depth about data classification and how to implement it, too. You can download the guide here.

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