Automating Safe Data Analytics for Financial Services

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Speakers from BigID, Privitar and Amazon Web Services

About this talk

The advent of the data privacy era compounds the compliance and regulatory challenges that have held back many financial services institutions from executing data-driven strategies for growth and innovation. There is growing competitive pressure to better understand and respond to customer opportunities based on insights from data that enterprises process themselves, as well as from external data sources. However, global privacy compliance regulations, decentralized privacy governance and the risk inherent in sensitive data have resulted in cumbersome data management practices blocking the ability to use certain data in analytics workloads. Automating privacy risk minimization strategies can enable financial institutions to realize the benefits of accelerating analytics usage and keeping costs in check through services from cloud providers like Amazon Web Services (AWS). Join us for an in-depth look at how BigID and Privitar come together in AWS to: – Streamline data discovery to identify all data that needs to be protected – Apply a wide range of data privacy protections to that data to minimize risk – Provide analytics applications with rich and safe data in the cloud to unlock value With these capabilities, global financial organizations are ultimately empowered to accelerate their time to data in a safe and compliant manner. Speakers: Ilya Epshteyn, Principal Solutions Architect, AWS Sebastien Cognet, Privacy Engineer, Privitar Sachin Khungar, Senior Technical Architect, BigID
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The world is waking up to privacy risk and privacy harms. There is a clear realisation that unless we incorporate privacy into every aspect of the data supply chain we run the risk of impeding innovation and exposing customers to harm. Privitar’s mission is to promote and facilitate the ethical and safe use of valuable data assets. Using leading privacy engineering techniques, we help companies get maximum value from data while preserving customer’s privacy.