GDPR, CCPA and beyond: How synthetic data can reduce the scope of stringent regulations

As many organizations are still discovering, compliance is complicated. Stringent regulations, like the GDPR and the CCPA, require multiple steps from numerous departments within an enterprise in order to achieve and maintain compliance. From understanding the regulations, implementing technologies that satisfy legal requirements, hiring qualified staff and training, to documentation updating and reporting – ongoing compliance can be costly and time intensive.

synthetic data

In fact, a report found that one-third of all enterprises (defined as businesses with 1000+ employees) spent more than $1 million on GDPR compliance alone.

As more states move to adopt GDPR-like regulations, such as California’s CCPA and Washington’s failed, but not forgotten Washington Privacy Act (WPA) legislation, organizations are having to look very closely at their data sets and make critical decisions to ensure compliance and data security.

But what can be done to minimize the scope of these stringent and wide-reaching regulations?

If an organization can identify all of its personal data, take it out of the data security and compliance equation completely – rending it useless to hackers, insider threats, and regulation scope – it can eliminate a huge amount of risk, and drastically the reduce the cost of compliance.

Enter synthetic data

Organizations like financial institutions and hospitals handle large quantities of extremely sensitive credit/debit card and personally identifiable information (PII). As such, they must navigate a very stringent set of compliance protocols – they can fall under the GDPR, CCPA, PCI DSS and additional laws and regulations depending on their location and the location of their customers.

Synthetic data is helping highly regulated companies safely use customer data to increase efficiencies or reduce operational costs, without falling under scope of stringent regulations.

Synthetic data makes this possible by removing identifiable characteristics of the institution, customer and transaction to create what is called a synthetic data set. Personally identifiable information is rendered unrecognizable by a one-way hash process that cannot be reversed. A cutting-edge data engine makes minor and random field changes to the original data, keeping the consumer identity and transaction associated with that consumer completely protected.

Once the data is synthetized, it’s impossible for a hacker or malicious insider to reverse-engineer the data. This makes the threat of a data breach a non-issue for even the largest enterprises. Importantly, this synthetic data set still keeps all the statistical value of the original data set, so that analysis and other data strategies may be safely conducted, such as AI algorithm feeding, target marketing and more.

What do the major data privacy regulations say about synthetic data

The CCPA does not expressly reference synthetic data, but it expressly excludes de-identified data from most of the CCPA’s requirements in cases where the requisite safeguards are in place. Synthesized data as defined is considered de-identified data. The CCPA also excludes from its coverage personal information subject to several federal privacy laws and comparable California state laws, including “personal information collected, processed, sold, or disclosed pursuant to Gramm-Leach-Bliley Act (GLBA) and the California Financial Information Privacy Act.”

Likewise, the GDPR does not expressly reference synthetic data, but it expressly says that it does not apply to anonymous information: according to UCL, “information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable.” Synthetic data is considered personal data which has been rendered anonymous and therefore falls outside the material scope of the GDPR.

Essentially, these important global regulatory mandates do not apply to collection, storage and use of synthesized data.

A big solution for big struggles

As businesses continue to grow in size and number of customers, the amount and frequency of data that flows in also increases dramatically. With these vast streams of data comes a struggle to collect, store and use customer data in a private and secure manner. This struggle is also becoming more publicly known, as headlines of data breaches or compliance violations flood news feeds seemingly every week.

To effectively and efficiently manage the influx of sensitive data while staying compliant and secure, companies can implement synthetic data in their environments with zero risks. Companies can use synthetic data to dig into customer action likelihood, analytics, customer segmentation for marketing, fraud detection trends, and more without jeopardizing compliance or data privacy.

And with data being the key to actualizing machine learning and artificial intelligence engines, companies can also utilize synthetic data to gain valuable insights into their algorithm data and design new products, reduce operational costs, and analyze new business endeavors while keeping customer privacy intact.

With the GDPR and the CCPA now in full effect and more industry and region-specific data regulations on the horizon, organizations of all sizes that want to reduce the burden of compliance will look to use synthetic data technology to manage their privacy and data security-related legal obligations.

Synthetic data helps organizations in highly regulated industries put customer data security and privacy first and keep their data operations frictionless and optimized while minimizing the scope of compliance. The more organizations that adopt synthetic data, the safer personal information transactions become, and the more organizations are free to conduct business without having to worry about regulation.

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GDPR, CCPA and beyond: How synthetic data can reduce the scope of stringent regulations