Once you decide to implement a data security strategy, it can be difficult to know where to start. With so many potential threats and challenges to resolve, teams often try to fix everything at once. But this boil-the-ocean approach is difficult to manage efficiently and ultimately leads to frustration, confusion, and halted progress. There's a better way to go. In this report, data science and AI leader Federico Castanedo shows you what to look for in a data security platform that will deliver the speed, scale, and agility you need to be successful in today's fast-paced, distributed data ecosystems. Unlike other resources that focus solely on data security concepts, this guide provides a road map for putting those concepts into practice. This report reveals: The most common data security use cases and their potential challenges What to look for in a data security solution that's built for speed and scale Why increasingly decentralized data architectures require centralized, dynamic data security mechanisms How to implement the steps required to put common use cases into production Methods for assessing risks—and controls necessary to mitigate those risks How to facilitate cross-functional collaboration to put data security into practice in a scalable, efficient way You'll examine the most common data security use cases that global enterprises across every industry aim to achieve, including the specific steps needed for implementation as well as the potential obstacles these use cases present. Federico Castanedo is a data science and AI leader with extensive experience in academia, industry, and startups. Having held leadership positions at DataRobot and Vodafone, he has a successful track record of leading high-performing data science teams and developing data science and AI products with business impact.
