September 14, 2022
Is There a Difference Between Data Privacy and Data Security?
For a poem or a song, a writer might receive enough creative license to use “vein” and “artery” interchangeably. But conflating those terms would be entirely unacceptable for a medical professional preparing for vascular surgery. In the same way, business leaders and IT professionals must make a distinction between data privacy and data security before they can prepare an adequate plan for either.
Data Privacy Vs. Data Security
To make the distinction between data security and data privacy, it helps to have definitions of both. We might start here:
- Data Privacy: Organizational adherence to regulations and best practices when collecting, processing, and sharing information.
- Data Security: All strategies, efforts, and technologies deployed to protect data from threats, whether internal or external.
There is some overlap in the Venn diagram of data privacy and data security. However, each is its own endeavor, with its own challenges and solutions. A complete approach to data management must address both. A cohesive approach to data management can address them together.
Figure: Venn Diagram showing data privacy and data security as two overlapping circles
Data privacy solutions identify, track, protect, and monitor data across multiple platforms and environments to provide enterprise-wide coverage. Much of this effort is focused on data privacy laws, which means keeping tabs on which by various regulations.
There are two primary requirements to keep in mind when collecting, sharing, and analyzing sensitive data:
- Complete discovery of all information within the organization
- Enough knowledge of privacy laws to determine which information is sensitive
The importance of data privacy requires delicate processes for analyzing and sharing sensitive information.
Analyzing Sensitive Data
If you put all of your sensitive information in a proverbial safe and throw away the key, it’s hard to get any value from the data. Privacy-enhancing technologies and anonymization techniques protect sensitive data in use while organizations perform secure analytics. Secure analytics extract insights from data to improve performance, without compromising the privacy of the dataset.
Sharing Sensitive Data
An enterprise’s ability to share data across geographical borders is limited by both global and local data sharing regulations. Some organizations might not have any issues here, as they have enough local staff to perform all development and testing. Enterprises that need to outsource any testing or development involving sensitive information to other parts of the world will have to find a way to do so without sensitive data ever leaving their (secure) data stores.
If data privacy is about governance and policy, data security is the execution of that policy: The “how” behind the “what.” A data security strategy is a series of defenses that protects an entire enterprise’s data landscape. Everything an enterprise deploys to counter threats across the entire organization falls under the umbrella of data security.
Data protection measures like masking, encryption, and tokenization keep sensitive data secure. Data subject access rights automation and database activity monitoring protect against unauthorized access and internal threats (including both accidental and intentional misuse of data). Complete data security accounts for all people, processes, and technologies interacting with sensitive data.
If You Can’t Access Data, Does it Still Yield Insights?
Analytics, testing, and development all require realistic data. Unfortunately, disseminating data to share access with analysts, testers, and developers exposes the data to more risk. Creating and managing synthetic test data gives companies supplemental information to use without putting protected data at risk. When synthetic test data is kept relevant and updated–without exposing any of the sensitive information from which it’s extracted–teams can make seamless transitions from production to non-production environments.
Security During Digital Transformation
Migration to the cloud is unlocking new capabilities for data storage and processing. Unfortunately, it also creates new exposure to threats like unauthorized access. The overwhelming majority of enterprises use at least one cloud service, which necessitates new layers in their data security strategies. Organizations must maintain referential integrity–as well as seamless protection–across all anonymized data in on-premise data storage, cloud services, and SaaS applications.
Does Data Security Exist to Serve Data Privacy?
Data privacy is one (important) application of data security, but not the only application. Data security strategies also protect valuable business data and proprietary information. Further, data security alone is not enough to satisfy data privacy requirements, which also require adherence to compliant practices for collecting, using, and sharing data. For example, enterprises must be transparent with individuals when gathering their data, telling them which data will be collected and for what purpose.
Where Data Privacy and Data Security Intersect
Sensitive data discovery is the first step for both data privacy and data security. Discovering the entirety of your enterprise data, especially sensitive information, illuminates what is necessary in terms of governance and protection. Identify the data, classify it according to risk or sensitivity, develop a plan for proper handling, then verify the security of that plan.
The next step is finding a way to bring everything together. How do data privacy efforts and data security efforts become synergistic parts of an integrated strategy? How do you apply a consistent data protection approach across on-prem, cloud, and Saas? Unified data security and data privacy platforms combine multiple solutions to make it all manageable.
Mage reimagined enterprise data privacy and security to lead the charge against sensitive data challenges. To learn how, schedule your free demo.