The precursor to any comprehensive data-centric security and governance tool is knowing exactly what to protect. Dataguise DgSecure offers industry leading sensitive data detection capability that can accurately determine the counts and exact location of all the sensitive data in the enterprise data repository.
Sensitive data is constantly flowing in and out of your enterprise—and to and from the cloud—whether you know it or not. As big data gets bigger and cloud usage increases, governing sensitive data and managing digital risk at an enterprise level are daunting challenges. Learn how you can get a single, comprehensive view of sensitive data governance across your enterprise.
Dataguise DgSecure delivers the most precise, data-centric security solution that detects, audits, protects, and monitors sensitive data in real time wherever it lives and moves across the enterprise and in the cloud. For Amazon Web Services (AWS) customers, it offers industry-first support for sensitive data stored in Amazon Simple Storage Service (Amazon S3) and accessed via Amazon Elastic MapReduce (Amazon EMR) for all your big data use cases.
Secure Business Execution is the ability of an organization to safely and responsibly leverage all of their data to gain new business insights, drive incremental revenue and maximize competitive advantage. Learn how Dataguise enables this essential differentiator for today’s data-driven enterprise.
Businesses today are more data-driven than ever, leveraging Hadoop to create new risk measurement products, reduce fraud, and accelerate customer insights. They are increasing their investment in MapR Technologies to drive more revenue-generating, market-facing insights and applications. At the same time, these organizations are expanding the likelihood that sensitive personal, financial, or health data may be exposed in Hadoop and or in the cloud.
Dataguise delivers the most precise security solution that detects, audits, protects, and monitors sensitive data assets using a 100% cloud-based, automated platform that secures HDInsight and other Hadoop Distributions available in the Azure Marketplace. As your organization brings data into Azure, you can define your sensitive data policies with Dataguise.
Dataguise delivers the most precise security solution that detects and protects sensitive data assets. As your organization brings data into SQL Server on Azure, you can define your sensitive data policies with Dataguise. Our one-stop solution will then detect the location and amount of all sensitive elements, optionally mask, redact, or scramble what shouldn’t be there, and ensure all of your sensitive PII, PCI, and HIPAA data in structured, semi-structured, and fully unstructured formats stays secure in the Azure cloud.
What is the bull’s-eye approach to data privacy protection for Hadoop? Organizations that need to make data-driven business decisions and also ensure compliance need a viable action plan to protect sensitive data with minimum business disruption. Download this white paper to learn about the 10 simple steps your organization can take to define an action plan that will enable you to securely unlock the power of big data in Hadoop.
What are the challenges of protecting big data environments? Download this white paper to learn about Dataguise’s innovative software that can assess your organization’s data-related risks, protect the sensitive data you have, and allow you to safely reap the tremendous benefits of data in the Hadoop framework.
Big data is taking companies by storm. Information is proliferating faster than organizations can manage it. Risks are heightening as enterprises share an ever-expanding trove of data with testers and data analysts. Download this white paper to learn how your organization can successfully detect and protect sensitive information in an increasingly challenging environment.
Samsung runs several of the world’s largest big data product analytics applications. To ensure worldwide global privacy protection, Samsung has deployed Dataguise across the globe to run continuous detection and encryption for the sensitive user-specific elements of their mobile phone logs.
A global leader in health insurance and healthcare provides health benefits and services to more than 85 million individuals in more than 125 countries. To detect and protect its customers’ Protected Health Information (PHI), the company turned to Dataguise to deliver an intuitive, automated data-centric solution that addresses security and compliance across their complex structured, semi-structured, and unstructured data feeding into their aggregate MapR data lake.
The world’s largest credit card issuer leverages big data analytics to detect fraudulent activity and improve the purchase experience for more than 90 million credit card holders across 127 countries. The company leverages Dataguise data-centric masking solutions to create multiple, tiered cluster views, ensuring automated protection at scale.
Navy Federal Credit Union aims to be the preferred and trusted financial institution serving the military and their families, growing from seven to more than five million members since 1933.The company uses Dataguise detection in conjunction with Compuware database masking to protect the private data of its members across large, complex, multi-vendor data in Oracle, Microsoft SQL, IBM DB2, Sybase, and MySQL.
An independent U.S. Government insurance agency needs to protect all Personal Identifiable Information (PII) data that is copied and accessed in non-production Oracle systems. Dataguise detection and masking successfully uncovers sensitive data during the staging of new non-production environments, and dynamically masks sensitive data in high-performance in-database agents.
Miri Infotech, a software development and integration company, was able to help customers realize time to value by leveraging Dataguise DgSecure and AWS solutions. By doing so, Miri was able to uncover sensitive data and secure new customers across multiple regulated verticals by setting up environment on AWS.
Dataguise Discovery is a fully automated software solution for discovering and detecting all personal, conﬁdential, and otherwise sensitive data amassed by an enterprise. In use at Fortune- 500 companies since 2007, it is, by far, the most technologically advanced and proven solution available today.
Enterprises today have so much data and so many different requirements to use their data that there is a high variability in how data is collected, where it is stored, and how it is used. The ramifications of such variability is that enterprises cannot just forklift their entire data repository to the cloud, but instead require a much finer control on how and when they can do so.
Even though big data and cloud computing have moved beyond the hype and into mainstream adoption, many companies hesitate to embark on cloud-based big data projects. This eBook explains how Dataguise and Microsoft help address the biggest obstacles to unlocking the benefits of big data in the cloud.
In this age of big data and the cloud, data security is no longer just an IT concern but a fundamental business issue. Boards of directors can be held responsible if organizations do not take adequate steps to reduce risk and protect confidential information. This eBook helps board members get a handle on their organization’s risk profile.
Data in big data platforms like Hadoop is vast, varied, and vague, making it difficult to keep secure using traditional methods. This eBook explains why detecting and protecting data at the data (or element) level is the only way to keep confidential information safe as organizations consume and expand access to more and more data across the enterprise.
Data modernization is an umbrella concept that involves recognition and adoption of newer systems that will make collection, storage, consumption and utilization of data more effective and efficient. This could imply fundamental changes at many levels of the data lifecycle for example, data that is rarely accessed could be migrated into a cheap low throughput cloud store, or data that is concurrently queried might be migrated into a highly optimized data warehouse.
It’s the process by which sensitive data within a data repository is located, identiﬁed, counted and reported. To keep things simple, we use the term “sensitive data” to describe any type of personal, private, conﬁdential or otherwise sensitive data or information. Knowing exactly what and where sensitive data exists in your organization at any give time is foundational to managing data privacy, security and risk, as well as maximizing data value.