De-identify and Secure Your Data Analytics Pipeline

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Presented by

Harold Byun, VP Products

About this talk

Multiple businesses continue to establish cloud-based data lakes for analytics, machine learning and artificial intelligence use cases. The flexibility of the cloud allows for an easier spin-up of resources and facilitates a faster data delivery model for the business. However, within the context of modern data privacy laws and compliance regulations, a significant challenge remains around how to de-identify data while still allowing for advanced analytics to occur to meet the demands of your organization's need to extract value from the personal data it collects. Many of methods for de-identification require additional development or altering the data pipeline and as a result either slow down the use of cloud-based analytics or leave data potentially exposed. Further, de-identification represents only part of the challenge as new methods to access and warehouse data can limit scenarios where authorized re-identification or analysis of data may be required. Attend this webinar to learn how data can be easily de-identified or tokenized as part of your data pipeline engineering. Learn how you can establish an S3 cloud data lake fully de-identified and use AWS Athena to easily access and run analytics on data sets for authorized users. This session will provide a review of some key data security gaps related to a data analytics pipeline, and provide a live demonstration of de-identifying data on the fly during a migration, as well as subsequent data analysis via AWS Athena. Join this webinar and learn about the following: - Common data security gaps for data sources in S3 - Key differences between existing cloud storage encryption methods and de-identification processes - Methods for de-identification, tokenization and encryption of data - Architecture models to support a de-identified data pipeline - Methods to allow for simplified analysis of data in a cloud data lake
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