As organizations of all sizes and across industries increasingly seek to use their data for analytics to drive innovation, they face a complex challenge: how can they accelerate analytics projects and extract value without compromising privacy?
Data scientists struggle to get quick access to sensitive data for analytics, and using one-size-fits-all de-identification protections leaves them with data that has insufficient utility for complex use cases. Using raw data for analytics enables data scientists to access high-utility data, but it also inherently increases risk.
There are three major risks organizations need to avoid:
Given the risks, it’s clear that data protection is essential, but it is also critical to maintain data’s usability for analytics.
Safe, usable data can be one of most valuable assets for any organization. Data can help organizations make better business decisions, innovate more rapidly, and increase the effectiveness of their engagement with customers.
How, then, can an organization create data that’s safe to use and analyze without incurring significant risk or losing data utility? Following are five key principles that organizations must consider to maximize the utility of their data while ensuring that it remains protected, alongside insights into how Privitar enables that process:
Safe data provisioning is accelerated by defining “rules of the road” for the lines of business to share data. These policies allow the privacy, security and compliance teams to set clear expectations about how data can be shared in the organization.
With Privitar, privacy policies can be defined and managed by a central team and enforced across your entire organization to maintain consistency and compliance with regulatory standards in every region where you operate. If you need to update a policy, you can make changes through a no-code, easy to use interface where updates can be applied automatically in your data processing platforms. If automated deployment is important, a Privitar API layer can support that, also.
Privacy techniques are often applied too broadly and data utility is lost during the de-identification process. Organizations without fine-grained control might use broad-brush techniques (such as encryption and redaction), indiscriminately destroying the data relationships that lead to valuable insights.
Privitar has more privacy techniques out of the box than any other technology, including data masking, tokenization, generalization, perturbation, redaction, substitution and encryption. With a range of options, and flexible privacy rules and policies, you can systematically apply the right protections for the right scenarios, based on the analysis you wish to apply to the dataset and the privacy risks associated with the data sharing scenario.
The modern data enterprise operates a variety of transactional and analytical data stores, as well as data pipelines across private, public, and hybrid clouds. Privacy protections need to work seamlessly across these existing data architectures.
Privitar integrates with leading technology providers including AWS, Azure, Google Cloud, Confluent, Cloudera, BigID and Collibra to deliver integrated product offerings and comprehensive data privacy solutions.
Our partnerships with leading infrastructure providers ensure customers can maximize their return on investments in big data and cloud technologies, ensuring privacy can be managed across complex, globally distributed environments. With native and API integrations to the full range of cloud and on-prem big data standards, and a commitment to remain vendor neutral, you can evolve your data pipeline and analytics environments without risking loss of support or vendor lock-in.
Tracing data that has been shared or leaked back to its origination point is essential to deter out of compliance sharing and enable forensic investigations after leaks.
Privitar embeds “watermarks” in the data itself, providing the ability to trace and audit the lineage of protected datasets without affecting fidelity or data bias. Watermarks are like digital fingerprints or tags that cannot be removed, enabling you to work with utility optimized datasets that are safe in the hands of internal and external users. Watermarks facilitate the detection and attribution if any unauthorized copies of data are made.
By leveraging data privacy technology, your organization can make sensitive data far more available, without compromising its safety, thus facilitating the democratization of data science. With privacy as an intrinsic part of your process, you make it possible to share safe data with larger groups within your organization and empower more of your workforce to perform high-leverage data science work.
Organizations want to democratize access to data, use more of their data for innovation, and reach insights faster; all while maintaining consumer trust and respecting customer privacy. By embracing strategies and adopting technologies that enable the above principles, organizations can accelerate the speed to deliver safe data to broader groups of data users and maximize data utility for insights and innovation.
Our team of data privacy experts is here to answer your questions and discuss how data privacy can fuel your business.