In this practical, informative white paper, you’ll learn:
- Data analytics and controls and risks associated with it
- Model governance, derived data, automated decisions and security analytics
- Data encryption pros, cons and emerging techniques
- Best practices for classifying information
- Where data science and data analytics are beginning to overlap
- Recommendations for big data policies and procedures
Big Data Introduction
The H-ISAC Big Data Controls Working Group (BDCWG) provides a venue for sharing information regarding the challenges and opportunities associated with big data systems. The working group is focused on identifying both information control best practices for big data systems and data analytics best practices for security and business applications. Additional information regarding the working group can be found in Appendix A.
This report is intended to provide a set of recommended information control best practices for big data environments and also raise awareness regarding some of the challenges associated with protecting information in these environments. The intended audience includes organizations that have implemented or are considering implementing a big data environment. Additionally, the information presented in this report serves to facilitate the alignment of various stakeholders in the big data space, including security professionals, big data engineers, data scientists, and consumers. The consumer groups associated with these systems includes business analysts, security analysts, data scientists, business operators, and decision makers. Unlike traditional database environments, modern big data environments generally support the storage and processing of a large variety of different data feeds and simultaneously serve as an analytic platform for generating new information from data stored within the environment. Appropriate controls must be established to protect information in these systems while still enabling analytic processing and access by a broad set of consumers.