Table of Contents
- Market Categories and Deployment Types
- Key Criteria Comparison
- GigaOm Radar
- Vendor Insights
- Analyst’s Take
- About Paul Stringfellow
Data is at the core of today’s enterprise, and ensuring it’s protected and secure is at the top of every enterprise’s agenda. The cost of data loss is significant and its impact wide ranging. This can be technical (with loss of services impacting operational ability), reputational (damaging relationships and future business success), and/or financial (from loss of business to regulatory fines). Preventing data loss is of paramount importance.
The complexity of the ways data is held today means companies must seek appropriate technology to reduce the risk of loss, and this is true for all businesses regardless of sector, size, or reputation. Finding solutions that address these challenges is essential.
Data loss prevention (DLP) tools operate on several levels. They must be able to determine the risks involved in data usage, whether by identifying sensitive data or by identifying uses of data that present a risk. They must offer mitigation measures when risk is identified to protect the security of the data and the information it contains. And, as enterprise infrastructure evolves, DLP tools must evolve as well, and be able to identify risks to data across many locations—in the data center, enterprise endpoints, and beyond into public cloud and software as a service (SaaS) applications. Increasingly, DLP tools must be risk- and threat-aware, and that awareness must include the risk and threat posed by those inside the enterprise security domain.
DLP vendors are responding to these needs. Increasingly, solutions are offered as a service from the public cloud, though many vendors still offer onsite solutions for those that need it. They’re taking advantage of new ways to integrate with different data repositories to ensure DLP remains effective.
Vendors are also recognizing the growing difficulty of using traditional DLP approaches to solve modern challenges, with many increasingly investing in the use of machine learning (ML) and artificial intelligence (AI) to more effectively and accurately identify risky behavior that poses a threat to data.
The threat of data loss continues to be high, and its potential impact on the enterprise is significant. But leading vendors are responding with levels of innovation that provide more effective protection as enterprises need to act immediately to get a complete picture of their data protection posture and address any shortcomings.
This Radar report reviews the leading DLP vendors, assessing their capabilities against criteria defined in the accompanying “Key Criteria Report for Data Loss Prevention Solutions.” This is an update of GigaOm’s 2021 report on DLP and it highlights the continued evolution of the market and describes the ways leading vendors have responded to changing data loss challenges and customer demands. Together, our reports give decision-makers an overview of the market, helping them evaluate current platforms and decide where to invest.
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding, consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.
Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.