Privacy-preserving Analytics: The Future of Data Protection Services & Data Security is Now
By Ameesh Divatia, CEO and co-founder | August 23, 2022
When Mark Campbell, the chief innovation officer at EVOTEK, asked me to discuss an article he was writing on the quest for mainstream adoption of privacy-preserving analytics, I was thrilled. Data privacy and privacy-preserving analytics have been a relevant topic for Baffle since 2019 when we were named a Gartner “Cool Vendor.” Three years ago, our data security mandate was this: Encrypt data in enterprise workflows without disrupting application operation. Our data protection services make the process to protect sensitive data at the record level, with row or column granularity, without impacting performance and allow comprehensive monitoring of that sensitive data.
Data Protection Solutions: We were ahead of the curve.
In his article, recently published in the IEEE Computer Society, Mark discusses the demand for a data protection strategy, one that protects data in use. He includes essential predictions from Gartner: “By 2025, at least 20% of companies will have a budget for projects that include fully homomorphic encryption, up from less than 1% today, and 60% of large organizations will use one or more privacy-enhancing computation techniques in analytics, business intelligence, or cloud computing.”
Mark recognizes Baffle and their team of security experts as one of the select few companies making privacy-preserving analytics a common architectural feature in applications and security services. In fact, we are joining forces with the likes of IBM and Microsoft to provide readily consumable products and services to keep pace with growing data-in-use protection demands.
Baffle currently protects more than 100 billion records for customers in financial services, healthcare, retail, industrial IoT, and government, running at scale. And we do so without any perceivable impact on application performance or user experience. Our underlying techniques add only a small percentage to the performance profile and allow databases, data warehouses, ingest pipelines, and visualization tools to operate in a “plaintext-free” environment. We view this methodology as a significant change in the data pipeline architecture, with security built-in rather than a security solution that is bolted on. Companies continue to move their data to where data analysis occurs, private, multi, or hybrid cloud environments. As such, data is more valuable and mobile, making its protection against exposure and vulnerability a top priority. Privacy-preserving analytics allows encrypted data to be processed while remaining unreadable and unusable to those without access. As Mark aptly stated, what was once a future vision is now “doomed to succeed.”