Data is one of the most valuable assets of any organization, but also one of the most vulnerable to breaches, theft, or misuse. To protect your data and comply with regulations, you need to classify it by risk level and apply appropriate security measures. In this article, we will explain how you can do that using a simple framework and some best practices.
Data classification is the process of organizing your data into categories based on its sensitivity, value, and legal requirements. Data classification helps you identify which data needs more protection, how to store it, who can access it, and how to handle it. Data classification can also help you reduce costs, improve performance, and optimize resources by allocating them according to data needs.
In our organization this is one of the most important factor and handle very professionally. ~~ Classify data by risk level and apply security measures using a systematic approach and tools. This includes automated data tagging, metadata analysis, and threat intelligence to assess risks. ~~Security measures involve access control, encryption, data masking, and monitoring with tools like SIEM and DLP. ~~ Data governance and compliance are enforced through frameworks and policies. Continuous improvement includes regular assessments and patch management. This approach ensures data protection, privacy, and compliance while adapting to evolving risks.
Data classification is a process that involves assigning risk levels to data based on various factors. High-risk data is the most sensitive and confidential, such as personal information or financial records, while medium-risk data is less sensitive but still valuable, like business plans or customer feedback. Low-risk data is the least sensitive and public, like marketing materials or website content. To classify your data by risk level, you need to consider the impact of data loss on your organization, customers, partners, or stakeholders; legal and regulatory obligations; frequency and volume of data usage and transfer; lifecycle and retention period of your data; and data sources and destinations. A data classification matrix or tool can help you assign risk levels to your data based on these factors.
Start by categorizing your data into different types or categories, such as personal, financial, intellectual property, proprietary, public, or other relevant classifications. This initial step will help you understand the nature of the data you're dealing with. Evaluate the sensitivity of each data category based on factors like confidentiality, integrity, and availability. Consider the potential consequences of unauthorized access, disclosure, alteration, or loss. Create a set of criteria or factors to assess the risk associated with each data category. These criteria can include Integrity, Availability, Legal and Regulatory Requirements.
Use a Data Dictionary. Besides tracking the data types, sizes scale - track comments, type-2 and PII / security risk. Assign a simple scale like High, Medium and Low. I've tried more detailed risk scale and it only confused people. Use the "traffic light metaphor" (red, green, yellow)
Once you have classified your data by risk level, you need to apply security measures to protect it from unauthorized access, modification, or destruction. These measures may include encryption, which transforms data into an unreadable format that can only be decrypted with a key; authentication, which verifies the identity of the users or systems that access your data; authorization, which grants or denies permissions to the users or systems that access your data; auditing, which records and monitors activities and events related to your data; and backup and recovery, which creates and restores copies of your data in case of loss, corruption, or disaster. You should apply security measures according to the risk level of your data - for example, high-risk data may require stronger encryption, stricter authentication and authorization, more frequent auditing, and more reliable backup and recovery than low-risk data.
To make your data classification and security effective and efficient, you should define and document your policies and procedures, educate and train your staff and stakeholders on the importance of data classification and security, review and update regularly, use tools and technologies that support and automate processes, and test and audit periodically. Doing so will help protect your data, comply with regulations, and enhance your data value and quality. Data classification and security are essential for any data architecture project, so it's important to follow these steps and best practices.