De-identifying Cloud Data Pipelines: Approaches, Best Practices, and Learnings - Seguridad Mania.com - España y América Latina

Cloud-based data lakes and data stores provide tremendous business value for next-gen analytics, machine learning and artificial intelligence, as well as sharing data with partners. However, they also present a large challenge for protecting the exploding volume of sensitive data that is being collected and moved to cloud platforms such as Amazon Redshift and Snowflake. A critical protection measure is de-identifying sensitive data as it is being migrated from on-prem sources to the cloud and staged for use in downstream applications. There are various approaches and considerations based on your use cases. Modern data security platforms provide cutting-edge solutions that are easy to implement and address today’s threats and attack vectors more comprehensively. Join this webinar and learn about: * Data de-identification methods and architectures * Comparing modern data protection platforms versus database native encryption * Data masking, tokenization, field and record level encryption, and object/file encryption * No-code solutions that require no application modifications * Ensuring high performance and minimal downtime * Role-based access control for secure data access and sharing * Privacy-enhanced computation and analytics