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Why you need a Data Inventory to secure your company

Cybersecurity Guide - Cybersecurity GRC

One of the most obvious but undervalued ways to improve data protection is by properly managing it. Most companies do not take the time to thoroughly inventory their data. In order to protect your data effectively, you need to know where your valuable and sensitive data resides.


Why you need a Data Inventory to secure your data

It is crucial that every organization establish a comprehensive data management process that encompasses a data management framework, data classification guidelines, and requirements for the protection, handling, retention, and disposal of data. Creating a successful process depends on creating an accurate and thorough data inventory. A data inventory is a comprehensive record of an organization's data assets, designed to facilitate the management, protection, and compliance of these assets.

Some examples of industries that rely heavily on data protection and significantly benefit from developing a data inventory include:

  1. Healthcare: Healthcare providers, insurers, and other entities in this industry handle sensitive patient data, such as electronic health records (EHRs) and personally identifiable information (PII). They must adhere to strict regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the United States.

  2. Financial services: Banks, credit unions, investment firms, and insurance companies handle sensitive financial data and PII. They must comply with regulations such as the Gramm-Leach-Bliley Act (GLBA) in the United States and the Payment Card Industry Data Security Standard (PCI DSS) globally.

  3. E-commerce and retail: Online retailers and brick-and-mortar stores that process credit card transactions must adhere to PCI DSS to protect customer payment data.

  4. Telecommunications: Telecommunication companies, internet service providers, and mobile network operators handle large volumes of sensitive customer data and communication records. They must comply with various privacy and data retention regulations, depending on the jurisdiction.

  5. Technology companies: Software, cloud service providers, and other technology companies often manage large amounts of user data, including PII. They must comply with data protection regulations such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States.

  6. Government agencies: Public sector organizations manage various types of sensitive data, including PII, national security information, and other classified data. They must comply with numerous government-specific data protection regulations and standards.

  7. Education: Schools, colleges, and universities handle student and employee data, including education records, personal information, and research data. They must comply with regulations such as the Family Educational Rights and Privacy Act (FERPA) in the United States.

  8. Legal services: Law firms and other legal service providers manage sensitive client data and confidential case information. They must adhere to legal professional privilege and data protection regulations in their respective jurisdictions.

Organizations across these industries must maintain a robust data inventory to ensure the proper identification, protection, and management of their data assets, helping them comply with regulatory requirements and safeguard the sensitive information they handle.

How to properly plan and develop your data inventory:

  1. Define the scope: Determine which data types, systems, and processes will be included in the inventory. Consider including all data assets, both structured and unstructured, across various departments, applications, and storage locations.

  2. Assemble a team: Form a cross-functional team with representatives from different departments, including IT, legal, compliance, and business units. This team will help ensure that all relevant perspectives are considered during the inventory process.

  3. Identify data sources: Catalog all data sources within the organization, such as databases, file systems, cloud storage, third-party services, and external data feeds. Be sure to include both on-premises and off-premises data storage.

  4. Collect data attributes: For each data source, gather information on data attributes, such as data type, format, sensitivity, owner, and usage. This information will help in understanding the data's importance and the risks associated with it.

  5. Classify data: Categorize the data based on its sensitivity and criticality, using labels such as "Public," "Confidential," and "Sensitive." This classification will help in determining the appropriate security measures and handling procedures for each data type.

  6. To determine data sensitivity levels, organizations must catalog their primary data types and assess the overall criticality, considering the potential impact of data loss or corruption. This assessment will inform the development of a tailored data classification scheme for the organization. Common labels used for classification include "Sensitive," "Confidential," and "Public," which can be applied to categorize data based on its sensitivity.

  7. It's important to note that each company can define their own set of labels. It's usually ideal to have as few labels as possible and keep the definitions straightforward. Military and Government classification schemes.

  8. Map data flows: Document how data moves within the organization, including data entry points, processing steps, storage locations, and sharing with external parties. This mapping will help identify potential risks and vulnerabilities in the data lifecycle.

  9. Assess data quality: Evaluate the accuracy, completeness, and consistency of the data. Identify any data quality issues and take steps to address them.

  10. Determine data ownership and responsibilities: Assign data ow