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Level Up - Automatically tokenize sensitive data with DLP and Dataflow
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Welcome to Level Up: From Zero - the show where we show you how to build solutions with Google Cloud Platform, hands-on.
In this episode, Solution Architect Anant Damle discusses using Dataflow and the Data Loss Prevention API to auto-tokenize and encrypt sensitive data. One of the daunting challenges during data migration to the cloud is how to manage sensitive data. The sensitive data can be in structured forms like analytics tables or unstructured like chat history or transcription records. You can use Cloud DLP to identify sensitive data from both of these kinds of sources and then tokenize the sensitive parts.
00:35 - Introduction and explanation of tokenization
01:35 - Using encryption with tokenized data
02:45 - Automatic tokenization architecture overview
04:26 - Step 1: Flatten & sample
06:26 - Step 2: Batch & identify
09:20 - Step 3: Tokenize
09:42 - Hands-on demo
13:03 - Wrap-up and resource links
Hands-on training with Qwiklabs
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In this episode, Solution Architect Anant Damle discusses using Dataflow and the Data Loss Prevention API to auto-tokenize and encrypt sensitive data. One of the daunting challenges during data migration to the cloud is how to manage sensitive data. The sensitive data can be in structured forms like analytics tables or unstructured like chat history or transcription records. You can use Cloud DLP to identify sensitive data from both of these kinds of sources and then tokenize the sensitive parts.
00:35 - Introduction and explanation of tokenization
01:35 - Using encryption with tokenized data
02:45 - Automatic tokenization architecture overview
04:26 - Step 1: Flatten & sample
06:26 - Step 2: Batch & identify
09:20 - Step 3: Tokenize
09:42 - Hands-on demo
13:03 - Wrap-up and resource links
Hands-on training with Qwiklabs
Follow us on Twitter
Follow us on Facebook
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