How to De-Identify Apache Kafka Data Streams

Presented by

Ameesh Divatia, CEO, Baffle and Syl Yelle, VP, Baffle

About this talk

Apache Kafka is a powerful platform for streaming data in real time for multiple use cases, including data analysis downstream. However, putting sensitive data in the clear can create massive security and compliance issues. The challenge is that legacy data encryption methods are not sufficient as they do not allow control over who can see what data, and can also impede downstream data analytics and third party applications. Join this webinar to learn how modern data protection approaches and technologies can be used to easily de-identify and re-identify Kafka data streams to share sensitive information securely between internal and external audiences and data domains. We will discuss: * De-identification techniques and use cases * Using Single Message Transform (SMT) to tokenize sensitive data * Role-based data access control * Architecture, components, and flow * Demo of Kafka data de-identification
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Baffle, the application data protection company, prevents data breaches by securing the end-to-end data access model for applications and databases. Using this method, the technology protects against some of the most recent high-profile attacks and vulnerabilities -including Spectre and Meltdown. With its patent-pending technology, Baffle is the only company that can enable encryption of data at-rest, in use, in memory and in the search index without impacting the application using AES encryption. Baffle is also the first company to enable secure data processing on a commercial application and database to guarantee data protection. Baffle has raised $10.5 million of financing; its investors include True Ventures, Envision Ventures, ServiceNow Ventures [NYSE: NOW], Thomvest Ventures, Engineering Capital, and Industry Ventures.