De-identifying Data in Snowflake and Amazon Redshift

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Presented by

Harold Byun, VP Products

About this talk

Industry analysts estimate that 75% of the world's data is projected to move to cloud and cloud analytics platforms such as Snowflake and Amazon Redshift. As businesses continue to collect and push more data into cloud-based data lakes for analytics, machine learning and artificial intelligence, the importance of integrating security into your data pipeline cannot be underestimated. However, security is often looked at as an impediment to the analytics world. As a result, devops and data science teams may look to bypass security measures that can slow down their ability to adopt modern analytics platforms. This presents a significant challenge 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. Attend this webinar to learn how data can be easily de-identified as part of your data pipeline as it is staged for use in Snowflake or Amazon Redshift. Learn how you can establish a fully de-identified cloud data lake and use adaptive security controls to secure the data while allowing analytics to run on protected data sets for authorized users. Join this webinar and learn about the following: - Common data security gaps for data sources in cloud storage and Amazon 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 - Enabling secure computation on data in cloud-based analytics platforms

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