As we navigate the ever-changing landscape of data privacy regulations, organizations should view privacy-related policies as a must-have, not a nice-to-have.<\/p>\n
In an increasingly data-centric world, the more information your organization handles, the greater the risk. Such risk includes experiencing cybersecurity<\/a> concerns, disclosing data to unauthorized personnel, and breaking compliance with data privacy regulations. The European Union\u2019s landmark General Data Protection Regulation<\/a> (GDPR), enacted in May 2018, set a global standard for safeguarding consumer data, influencing privacy policies worldwide.<\/p>\n While the United States lacks a comparable federal law, over a dozen states have established their own data privacy measures. These grant individuals more control over their personal information, and other states are rushing to offer the same protection.<\/p>\n If you work with EU clients, you must comply with GDPR. If you operate in one or more states with privacy legislation, you must sift through the eleven different state-level privacy regulations to ensure you follow their guidelines.<\/strong> Even if you operate in states without such laws, we advise you pursue compliance with them, as your state may propose similar legislation. Regardless of location, data minimization is a crucial strategy to fortify your organization and protect consumer privacy.<\/p>\n To lower the risk sensitive data presents, you must reduce the amount of data your organization stores and processes. Data minimization is the practice of reducing the amount of personal data you collect to only what you need for business.<\/strong><\/p>\n Consequently, you\u2019ll need to securely dispose of all other sensitive information<\/a>, whether irrelevant or outdated. For example, an e-commerce platform may retain customer names, shipping addresses, and purchasing history for order processing. Meanwhile, they may eliminate retention of customer credit card information.<\/p>\n Minimizing the amount of sensitive data your organization manages not only reduces storage costs but lowers your risk of improper disclosure.<\/p>\n Currently, GDPR Article 5(1)(c), as well as California, Colorado, Connecticut, Utah, and Virginia state legislature all require data minimization. These policies instruct organizations to disclose what data they process and store, why they need it, and how they use it.<\/p>\n One popular strategy for data minimization is to go on a “data diet.” Equating data minimization to a diet gives employees a reference they can relate to. When people diet, they view food through a “need” lens. Dieters may ask, \u201cDo I need this food? Will this food provide what I need to achieve my health and wellness goals?”<\/p>\n When a person works to minimize sensitive data, they should ask, \u201cDo we need this sensitive data to conduct business right now? Will we need this sensitive data to conduct business in the future?\u201d<\/strong><\/p>\n If the answer is no, sensitive data should be securely disposed of through encryption or permanent deletion.<\/p>\n If the answer to either question is yes, you should not minimize the data. Rather, you should reduce its visibility<\/a>. Every time an employee views sensitive information, they generate additional risks for your organization. Your goal is to use sensitive information in a way that allows your business to function while minimizing its exposure. The fewest number of people possible helps mitigate risk.<\/p>\n Organizations can limit sensitive data visibility through data masking. This substitutes real data for a secure alternative, thereby securing its integrity. There are three approaches to data masking. These include static, dynamic, and on-the-fly data masking. The approach depends on the situation in which sensitive information could be exposed.<\/p>\n Data is masked in the original database and copied into a secondary database, usually a test environment. To limit who views sensitive data, users view masked data in the test environment, not real data stored in the original database.<\/strong><\/p>\n For example, say an employee named Diana is testing a new software application. She cannot access the original sensitive data, so she uses the masked copy to conduct her tests. This gives Diana high-quality, representative data without disclosing sensitive information like names or birthdates.<\/p>\n Data is hidden in real-time when people ask the database for information. Only authorized users can access sensitive data. Dynamic data masking works best when you only need to read data, not change it.<\/p>\n Mark, a customer service representative, requests information from the database (also known as a SQL query) he doesn’t need to conduct his job. The database proxy identifies Mark as unauthorized to view sensitive data. It modifies his request so that he sees only masked data and the original data remains safe.<\/p>\n Like dynamic data masking, data is masked in real time. But here it’s masked in the database application\u2019s temporary random-access memory (RAM) storage, not permanent storage. This means the database application can perform tasks without direct access to sensitive information, adding an extra security layer.<\/p>\n For example, Whitney uses an audit application to conduct an internal audit in her HR department. The audit application needs access to a database with potentially sensitive data. Its script ensures sensitive information the app requests is masked before displaying to an unauthorized user like Whitney.<\/p>\n Data scrambling is a similar approach to reduce data visibility. This involves obscuring or removing sensitive data altogether to make it unrecognizable.<\/strong> This process can involve encryption, randomization, or other techniques. It ensures that if an unauthorized person gains access to scrambled data, they cannot recover the original information. Scrambling is often used when the priority isn\u2019t data\u2019s usability but its security and impenetrability<\/a>.<\/p>\n Unlike data masking, which is temporary, data scrambling is a permanent process. As a result, the sensitive data is only accessible when duplicated. Unlike data masking, scrambled data does not always retain its structural integrity. This means that the data\u2019s format and structure might not last after scrambling.<\/p>\n By minimizing sensitive data usage and restricting access to it as much as possible, your organization will be poised to achieve compliance with existing privacy legislation, prepare for upcoming regulations in other states, and lower its overall risk.<\/p>\n\n What is Data Minimization?<\/h2>\n
How to Minimize Your Data<\/h2>\n
Data Masking to Improve Data Privacy<\/h3>\n
Static Data Masking<\/h4>\n
Dynamic Data Masking<\/h4>\n
On-the-Fly Data Masking<\/h4>\n
Data Scrambling for More Permanent Privacy<\/h2>\n
Conclusion<\/h2>\n