Key Criteria for Evaluating Data Loss Prevention Platformsv1.0

1. Summary

This Key Criteria report evaluates the purpose and capabilities of modern data loss prevention (DLP) platforms, providing insight into the current state of this established yet rapidly evolving sector.

With the continued rise in the business value of data, “bad actors” are developing new and increasingly covert ways to exfiltrate companies’ data or lock them out of that data completely. Now more than ever, employing an effective DLP strategy not only protects businesses against financial loss but also safeguards them from falling afoul of increasingly stringent data protection laws.

Beginning with an in-depth yet easy-to-understand primer on the ways in which companies find themselves at risk of a data breach and how technology can be used to mitigate such risk, this report provides a framework for evaluating the wide range of DLP solutions available.

Through an exploration of the key tenets of DLP, readers will gain a solid understanding of how to go about building a data loss prevention strategy for their business that meets the needs and regulatory requirements of a modern, forward-thinking enterprise.

Using GigaOm’s Key Criteria methodology, the report demonstrates what every business should expect of any player within this space—the features and technologies that differentiate players and the emerging technologies that will drive the growth and evolution of DLP platforms in the medium to long term.

Read the full report to:

• Explore the range of data loss and attack vectors, and the strategies that DLP platforms implement to secure them.
• Learn why internal threats pose just as much risk as external bad actors, and how to mitigate that risk by embracing a “zero trust” security model.
• See how DLP platforms are taking over the responsibility of educating users on the importance and best practices of data loss prevention, saving organizations and their security teams time and money.
• Understand the ways in which artificial intelligence and machine learning are driving innovation in this sector, leading to platforms that produce better results even as they reduce costs.

Author Paul Stringfellow is an analyst and data security practitioner with over 25 years of experience, working with a broad range of organizations from start-ups to multinational corporations on a variety of data projects. A respected voice in the world of data security, his hands-on approach provides valuable and unique insights into the world of enterprise technology.