Ibm Spectrum Scale Functionality To Support Gdpr Requirements
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| Author | : Sandeep R. Patil |
| Publisher | : IBM Redbooks |
| Total Pages | : 12 |
| Release | : 2018-04-24 |
| Genre | : Computers |
| ISBN | : 0738456764 |
The role of the IT solutions is to enforce the correct handling of personal data using processes developed by the establishment. Each element of the solution stack must address the objectives as appropriate to the data that it handles. Typically, personal data exists either in the form of structured data (like databases) or unstructured data (like files, text, documents, and so on.). This IBM Redbooks publication specifically deals with unstructured data and storage systems used to host unstructured data. For unstructured data storage in particular, some key attributes enable the overall solution to support compliance with the EU General Data Protection Regulation (GDPR). Because personal data subject to GDPR is commonly stored in an unstructured data format, a scale out file system like IBM Spectrum Scale provides essential functions to support GDPR requirements. This paper highlights some of the key compliance requirements and explains how IBM Spectrum Scale helps to address them.
| Author | : Sandeep R Patil |
| Publisher | : IBM Redbooks |
| Total Pages | : 32 |
| Release | : 2019-01-11 |
| Genre | : Computers |
| ISBN | : 0738457396 |
Having the appropriate storage for hosting business critical data and the proper analytic software for deep inspection of that data is becoming necessary to get deeper insights into the data so that users can categorize which data qualifies for compliance. This IBM® RedpaperTM publication explains why the storage features of IBM SpectrumTM Scale, when combined with the data analysis and categorization features of IBM StoredIQ®, provide an excellent platform for hosting unstructured business data that is subject to regulatory compliance guidelines, such as General Data Protection Regulation (GDPR). In this paper, we describe how IBM StoredIQ can be used to identify files that are stored in an IBM Spectrum ScaleTM file system that include personal information, such as phone numbers. These files can be secured in another file system partition by encrypting those files by using IBM Spectrum Scale functions. Encrypting files prevents unauthorized access to those files because only users that can access the encryption key can decrypt those files. This paper is intended for chief technology officers, solution, and security architects and systems administrators.
| Author | : Boudhayan Chakrabarty |
| Publisher | : IBM Redbooks |
| Total Pages | : 68 |
| Release | : 2021-09-13 |
| Genre | : Computers |
| ISBN | : 073846001X |
Having appropriate storage for hosting business-critical data and advanced Security Information and Event Management (SIEM) software for deep inspection, detection, and prioritization of threats has become a necessity for any business. This IBM® Redpaper publication explains how the storage features of IBM Spectrum® Scale, when combined with the log analysis, deep inspection, and detection of threats that are provided by IBM QRadar®, help reduce the impact of incidents on business data. Such integration provides an excellent platform for hosting unstructured business data that is subject to regulatory compliance requirements. This paper describes how IBM Spectrum Scale File Audit Logging can be integrated with IBM QRadar. Using IBM QRadar, an administrator can monitor, inspect, detect, and derive insights for identifying potential threats to the data that is stored on IBM Spectrum Scale. When the threats are identified, you can quickly act on them to mitigate or reduce the impact of incidents. We further demonstrate how the threat detection by IBM QRadar can proactively trigger data snapshots or cyber resiliency workflow in IBM Spectrum Scale to protect the data during threat. This third edition has added the section "Ransomware threat detection", where we describe a ransomware attack scenario within an environment to leverage IBM Spectrum Scale File Audit logs integration with IBM QRadar. This paper is intended for chief technology officers, solution engineers, security architects, and systems administrators. This paper assumes a basic understanding of IBM Spectrum Scale and IBM QRadar and their administration.
| Author | : Sandeep R. Patil |
| Publisher | : IBM Redbooks |
| Total Pages | : 18 |
| Release | : 2020-03-16 |
| Genre | : Computers |
| ISBN | : 0738458600 |
When technology workloads process healthcare data, it is important to understand Health Insurance Portability and Accountability Act (HIPAA) compliance and what it means for the technology infrastructure in general and storage in particular. HIPAA is US legislation that was signed into law in 1996. HIPAA was enacted to protect health insurance coverage, but was later extended to ensure protection and privacy of electronic health records and transactions. In simple terms, it was instituted to modernize the exchange of healthcare information and how the Personally Identifiable Information (PII) that is maintained by the healthcare and healthcare-related industries are safeguarded. From a technology perspective, one of the core requirements of HIPAA is the protection of Electronic Protected Health Information (ePHIPer through physical, technical, and administrative defenses. From a non-compliance perspective, the Health Information Technology for Economic and Clinical Health Act (HITECH) added protections to HIPAA and increased penalties $100 USD - $50,000 USD per violation. Today, HIPAA-compliant solutions are a norm in the healthcare industry worldwide. This IBM® Redpaper publication describes HIPPA compliance requirements for storage and how security enhanced software-defined storage is designed to help meet those requirements. We correlate how Software Defined IBM Spectrum® Scale security features address the safeguards that are specified by the HIPAA Security Rule.
| Author | : Larry Coyne |
| Publisher | : IBM Redbooks |
| Total Pages | : 158 |
| Release | : 2018-07-21 |
| Genre | : Computers |
| ISBN | : 0738457051 |
Today, new business models in the marketplace coexist with traditional ones and their well-established IT architectures. They generate new business needs and new IT requirements that can only be satisfied by new service models and new technological approaches. These changes are reshaping traditional IT concepts. Cloud in its three main variants (Public, Hybrid, and Private) represents the major and most viable answer to those IT requirements, and software-defined infrastructure (SDI) is its major technological enabler. IBM® technology, with its rich and complete set of storage hardware and software products, supports SDI both in an open standard framework and in other vendors' environments. IBM services are able to deliver solutions to the customers with their extensive knowledge of the topic and the experiences gained in partnership with clients. This IBM RedpaperTM publication focuses on software-defined storage (SDS) and IBM Storage Systems product offerings for software-defined environments (SDEs). It also provides use case examples across various industries that cover different client needs, proposed solutions, and results. This paper can help you to understand current organizational capabilities and challenges, and to identify specific business objectives to be achieved by implementing an SDS solution in your enterprise.
| Author | : Bert Dufrasne |
| Publisher | : IBM Redbooks |
| Total Pages | : 508 |
| Release | : 2021-03-31 |
| Genre | : Computers |
| ISBN | : 0738459194 |
IBM® Spectrum Protect Plus is a data protection solution that provides near-instant recovery, replication, retention management, and reuse for virtual machines, databases, and applications backups in hybrid multicloud environments. IBM Knowledge Center for IBM Spectrum® Protect Plus provides extensive documentation for installation, deployment, and usage. In addition, build and size an IBM Spectrum Protect Plus solution. The goal of this IBM Redpaper® publication is to summarize and complement the available information by providing useful hints and tips that are based on the authors' practical experience in installing and supporting IBM Spectrum Protect Plus in customer environments. Over time, our aim is to compile a set of best practices that cover all aspects of the product, from planning and installation to tuning, maintenance, and troubleshooting.
| Author | : Nils Haustein |
| Publisher | : IBM Redbooks |
| Total Pages | : 40 |
| Release | : 2021-06-09 |
| Genre | : Computers |
| ISBN | : 0738459771 |
This IBM RedpaperTM publication introduces the IBM Spectrum Scale immutability function. It shows how to set it up and presents different ways for managing immutable and append-only files. This publication also provides guidance for implementing IT security aspects in an IBM Spectrum Scale cluster by addressing regulatory requirements. It also describes two typical use cases for managing immutable files. One use case involves applications that manage file immutability; the other use case presents a solution to automatically set files to immutable within a IBM Spectrum Scale immutable fileset.
| Author | : Joseph Dain |
| Publisher | : IBM Redbooks |
| Total Pages | : 108 |
| Release | : 2020-08-11 |
| Genre | : Computers |
| ISBN | : 073845902X |
This IBM® Redpaper publication explains how IBM Spectrum® Discover integrates with the IBM Watson® Knowledge Catalog (WKC) component of IBM Cloud® Pak for Data (IBM CP4D) to make the enriched catalog content in IBM Spectrum Discover along with the associated data available in WKC and IBM CP4D. From an end-to-end IBM solution point of view, IBM CP4D and WKC provide state-of-the-art data governance, collaboration, and artificial intelligence (AI) and analytics tools, and IBM Spectrum Discover complements these features by adding support for unstructured data on large-scale file and object storage systems on premises and in the cloud. Many organizations face challenges to manage unstructured data. Some challenges that companies face include: Pinpointing and activating relevant data for large-scale analytics, machine learning (ML) and deep learning (DL) workloads. Lacking the fine-grained visibility that is needed to map data to business priorities. Removing redundant, obsolete, and trivial (ROT) data and identifying data that can be moved to a lower-cost storage tier. Identifying and classifying sensitive data as it relates to various compliance mandates, such as the General Data Privacy Regulation (GDPR), Payment Card Industry Data Security Standards (PCI-DSS), and the Health Information Portability and Accountability Act (HIPAA). This paper describes how IBM Spectrum Discover provides seamless integration of data in IBM Storage with IBM Watson Knowledge Catalog (WKC). Features include: Event-based cataloging and tagging of unstructured data across the enterprise. Automatically inspecting and classifying over 1000 unstructured data types, including genomics and imaging specific file formats. Automatically registering assets with WKC based on IBM Spectrum Discover search and filter criteria, and by using assets in IBM CP4D. Enforcing data governance policies in WKC in IBM CP4D based on insights from IBM Spectrum Discover, and using assets in IBM CP4D. Several in-depth use cases are used that show examples of healthcare, life sciences, and financial services. IBM Spectrum Discover integration with WKC enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of data. The integration improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research.
| Author | : Clarence Pouthier |
| Publisher | : IBM Redbooks |
| Total Pages | : 24 |
| Release | : 2017-10-03 |
| Genre | : Computers |
| ISBN | : 0738456365 |
The Payment Card Industry Data Security Standard (PCI-DSS) is the global information security standard for organizations that process, store, or transmit data with any of the major credit card brands. More and more organizations are looking for compliance with this standard. This IBM® RedpaperTM describes how the features and functions of IBM SpectrumTM Virtualize help organizations towards compliance of their IT infrastructure on relevant areas of the PCI-DSS standard. IBM Spectrum VirtualizeTM is the software common to all IBM Storwize® products such as IBM SAN Volume Controller (SVC), IBM Storwize V5000 family, IBM Storwize V7000, IBM FlashSystem® V9000, and IBM Spectrum Virtualize as Software. Therefore, all recommendations in this paper equally apply to these storage products.
| Author | : Joseph Dain |
| Publisher | : IBM Redbooks |
| Total Pages | : 152 |
| Release | : 2019-10-01 |
| Genre | : Computers |
| ISBN | : 0738457868 |
This IBM® Redpaper publication provides a comprehensive overview of the IBM Spectrum® Discover metadata management software platform. We give a detailed explanation of how the product creates, collects, and analyzes metadata. Several in-depth use cases are used that show examples of analytics, governance, and optimization. We also provide step-by-step information to install and set up the IBM Spectrum Discover trial environment. More than 80% of all data that is collected by organizations is not in a standard relational database. Instead, it is trapped in unstructured documents, social media posts, machine logs, and so on. Many organizations face significant challenges to manage this deluge of unstructured data such as: Pinpointing and activating relevant data for large-scale analytics Lacking the fine-grained visibility that is needed to map data to business priorities Removing redundant, obsolete, and trivial (ROT) data Identifying and classifying sensitive data IBM Spectrum Discover is a modern metadata management software that provides data insight for petabyte-scale file and Object Storage, storage on premises, and in the cloud. This software enables organizations to make better business decisions and gain and maintain a competitive advantage. IBM Spectrum Discover provides a rich metadata layer that enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of unstructured data. It improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research.
