Data minimization is the data diet your company needs to reduce the amount of sensitive data you use. It’s a smart way to achieve compliance with the complex patchwork of data privacy regulations.
As we navigate the ever-changing regulatory landscape of data privacy, organizations should view privacy-related policies and oversight as a must-have, not a nice-to-have.
In an increasingly data-centric world, the more information your organization handles, the greater the risks of experiencing cybersecurity concerns, disclosing data to unauthorized personnel, and breaking compliance with data privacy regulations. The European Union’s landmark General Data Protection Regulation (GDPR), enacted in May 2018, set a global standard for safeguarding consumer data, influencing privacy policies worldwide.
While the United States lacks a comparable federal law, around a dozen states have established their own data privacy measures, granting individuals more control over their personal information, and many other states are rushing to do the same.
If you work with EU clients, you must comply with GDPR. If you operate in one or more states with specific privacy legislation, you must sift through the eleven different state-level privacy regulations to ensure you follow their guidelines. Even if you operate in states without such laws, we advise you to pursue compliance with them, as your state may propose similar legislation soon. Regardless of your location, data minimization is a crucial strategy to fortify your organization and protect consumer privacy.
What is Data Minimization?
To lower the risk your sensitive data presents, you must reduce the amount of data your organization stores and uses. Data minimization is the practice of reducing the amount of personal data you collect and process to only what you need for business.
Consequently, you’ll need to securely dispose of all other sensitive information, whether irrelevant or outdated. For example, an e-commerce platform may retain customer names, shipping addresses, and purchasing history for order processing while eliminating a customer’s credit card information.
Minimizing the amount of sensitive data your organization manages not only reduces storage costs, but, more importantly, lowers an organization’s risk of improper disclosure.
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 access to it, and how they use it.
How to Minimize Your Data
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, asking, “Do I need this food? Will this food provide what I need to achieve my health and wellness goals?
Similarly, when a person works to minimize sensitive data, they should ask, “Do we need this sensitive data to conduct business right now? Will we need this sensitive data to conduct business in the future?”
If the answer is no, the sensitive data should be securely disposed of through encryption or permanent deletion.
If the answer to either question is yes, you should not minimize the data. Rather, you should reduce its visibility. Every time an employee views sensitive information, they generate additional risks for your organization. To mitigate risks, your goal is to use sensitive information in a way that allows your business to function while minimizing its exposure to the fewest number of people.
Data Masking to Improve Data Privacy
Organizations can limit sensitive data visibility through data masking, which substitutes real data for a secure alternative when it is not necessary, thereby securing its integrity. There are three approaches to data masking, depending on the situation in which sensitive information could be exposed:
Static Data Masking
Data is masked in the original database and copied into a secondary database, usually a test environment. To limit who can view the sensitive data, users can view the masked data stored in the test environment, not the real data stored in the original database.
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 such as names or birthdates.
Dynamic Data Masking
Data is hidden in real-time when people ask the database for information so only authorized users can access the sensitive data. Dynamic data masking works best when you only need to read the data, not change it.
For example, if Mark, a customer service representative, requests information from the database (also known as submitting a SQL query), he does not need to conduct his job. The database proxy identifies Mark as unauthorized to view sensitive data and modifies his request so that he sees only masked data and the original data remains safe.
On-the-Fly Data Masking
Like dynamic data masking, data is masked in real time, but it is masked in the database application’s temporary random access memory (RAM) storage, not in its permanent storage. This means that the database application can perform its tasks without direct access to sensitive information, adding an additional layer of security.
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, but its script ensures any sensitive information the application requests is masked before it is displayed to someone who isn’t authorized to access it, like Whitney.
Data Scrambling for More Permanent Privacy
A similar approach to reduce data visibility is data scrambling, which involves obscuring or removing sensitive data altogether to make it unrecognizable. This process can involve encryption, randomization, or other techniques to ensure that even if an unauthorized person gains access to the scrambled data, they cannot recover the original sensitive information from it. Scrambling is often used when the priority isn’t the data’s usability but rather its security and impenetrability.
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, meaning that the data’s format and structure might not last after scrambling.
Conclusion
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.
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