Managing Data Privacy in the Age of Digital Transformation

Presented by

Brian Herr, VP Product Marketing, Privitar and Bernard Swierczyna, CISO, First Ireland Risk Management

About this talk

As enterprises evolve to become data-driven organizations, they are transitioning from simply collecting and storing data to democratizing data use in order to drive better decision making organization-wide. The path to data-driven decision making is made complex by regulations that dictate when, where, and how data can be used by those that collect it. In order to simultaneously optimize data utility and privacy, organizations must contemplate a flexible service that can protect that data in a multitude of ways based on the usage context. As companies begin to identify what they need from their data it will quickly become clear that the ultimate solutions needs to provide safe access to data that drives better outcomes and also respects privacy regulations. In this webinar, we will discuss how this is made possible by utilizing the right data privacy platform. We will examine how to get the most out of your cloud, big data or data lake initiative by creating a Safe Data service that will allow your organization to safely utilize sensitive data to make better decisions at the speed of business. Key Takeaways: - Examine the data and ways in which companies are looking to leverage data today. - Review of regulations and their impact on how companies can use data - Data Privacy and Security solutions are both required to create a sensitive data strategy. - Best practices for creating a Safe Data Service inside your organization.
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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.