Privitar Joins Confluent Partner Program to Integrate Best in Class Data Streaming and Privacy Engineering
Partnership responds to huge demand across industry for seamless data protection on streaming data
Privitar joined the Confluent Partner program today, which will make Privitar’s advanced privacy engineering features directly available into the Confluent streaming platform. The two companies have now begun the process of integrating Privitar’s data privacy software with Confluent Platform. The integration will provide customers with a best in class data streaming platform with best in class data privacy, de-identification and governance capabilities.
Confluent Platform improves Apache Kafka by expanding its integration capabilities, adding tools to optimize and manage Kafka clusters, as well as methods to ensure the streams are secure. Privitar’s product will slot into this architecture, maintaining the speed of Confluent’s data flow while de-identifying streaming stored data and making it even more secure.
Jason du Preez, CEO at Privitar, commented:
“Enhancing data privacy has never been more important to business leaders. A wave of regulation around the world and heightened awareness amongst consumers is bearing heavily on the shoulders of CEOs and their management teams. Unsurprisingly, businesses are now looking for advanced privacy engineering to be integrated into platforms such as those provided by Confluent. Some of the biggest brand names in data-driven enterprise use Confluent Platform to process streaming data to power real time analytics, business intelligence and machine learning applications at maximum speeds. Our products ensures sensitive data – be that financial, patient, customer or location – can be seamlessly protected in these data pipelines.”
Jason McFall, CTO at Privitar added:
“Data architectures are modernising rapidly, with streaming processing emerging as a powerful and flexible way to manage data. As architectures change, responsible organisations are seeking to apply privacy by design and data minimisation principles from the very outset. The integration of Privitar and Confluent’s capabilities will allow enterprises to build data privacy protection into streaming architectures, enabling regulatory compliance through strict governance of sensitive data. This combined product offering will empower organisations to manage privacy policies centrally and automatically apply them across their entire data estate, consistently protecting and watermarking sensitive data.”
Sid Rabindran, Director, Partner Ecosystem at Confluent, commented:
“As our customers use our streaming platform as a central nervous system, data protection becomes even more of a priority. Our partnership with Privitar means they will be able to leverage comprehensive data protection controls with the ability to enforce Privitar privacy policies as part of Confluent data flows. This makes it easy for our customers to reduce the risk associated with broader use of sensitive information and comply with a rapidly evolving regulatory landscape, all in real time.”
The partnership is another milestone in Privitar’s US and global expansion. The business, which started providing privacy software engineering to clients in 2016 was set up by serial entrepreneur Jason du Preez in London. Responding to the demands of its multi-national corporate clients – particularly in regulated industries such as financial services, life sciences and telecommunications – the business opened an office in Paris earlier in 2018 and opened its first office in the US, in New York, in June.
Jason du Preez concluded:
“Our partnership with Confluent fits with the growing trend of specialist technology companies working collegiately to ensure deployments are more streamlined, reducing risk for our customers. At Privitar, it has always been our goal to support our clients’ data analytics’ ambitions with comprehensive data privacy solutions. We are actively investing in partnerships that support our customers’ requirements to have privacy engineered into all aspects of modern data architectures.”
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