As concerns over data privacy and data theft continue to mount, companies are searching for solutions to help secure their customers' data and mitigate the risk of data theft, all without breaking business processes. The more recent data privacy regulations will significantly increase the cost of non-compliance and have the potential to stifle business intelligence and big data efforts as well. The challenge that companies face is implementing the appropriate data level security without adversely impacting the business while still facilitating ways to leverage and monetize information. Historically, achieving this type of balance was impossible and methods to appropriately secure data often broke applications or made data useless for analytics. Modern approaches to privacy preserving analytics are now available to easily protect your data for compliance, yet still allow for data processing on information without ever violating data privacy. Attend this webinar to learn how you can perform analytics and share data while still ensuring the appropriate level of data security and privacy. This session will provide a review of various data-centric protection methods (data masking, tokenization, BYOK, field and record level encryption) including privacy preserving analytics and how they can be easily implemented to protect information in the cloud and on-premise. Join this webinar and learn about the following: - Key requirements of recent data privacy regulations and compliance strategies - Common threat models that unknowingly leave data exposed to data theft - Various data-centric protection methods that can help mitigate data security risks - Privacy preserving analytics use cases that can

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