Many infrastructure and platform-as-a-service (PaaS) providers do a great job providing data warehouses and analytics solutions that offer agility and deliver fast results for business intelligence initiatives. In fact, industry analysts estimate that 75% of the world's data is projected to move to cloud and cloud-based analytics platforms such as Amazon Redshift.
These providers also support several methods to make it easy to get your data into the cloud. But what do you do when you need to keep your data private or ensure compliance with privacy regulations? How do those requirements impact your data pipeline architecture and operations? And, once your data is "in cloud", how do you control access and implement stronger security controls?
Attend this webinar to learn how data can be easily de-identified on-the-fly as part of your data pipeline process as it is staged for use in Amazon Redshift or cloud data lakes. 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
- How to avoid making more copies of your sensitive data during the pipeline process
- 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