Standard

ISO/IEC FDIS 29128-2 Information security, cybersecurity and privacy protection — Verification of Cryptographic Protocols Part 2: Evaluation Methods and Activities for Cryptographic Protocols

This document defines the evaluation methods and activities to assess the artefacts defined in Part 1 for the verification of the correctness and security of a cryptographic protocol specification using the framework from ISO/IEC 15408-4.

ISO/IEC 18045:2022 Information security, cybersecurity and privacy protection — Evaluation criteria for IT security — Methodology for IT security evaluation

This document defines the minimum actions to be performed by an evaluator in order to conduct an ISO/IEC 15408 series evaluation, using the criteria and evaluation evidence defined in the ISO/IEC 15408 series.

> Expected to be replaced by ISO/IEC 18045 within the coming months.

ISO/IEC 15408-5:2026 Evaluation criteria for IT security PART 5 Predefined packages of security requirements

This document provides packages of security assurance and security functional requirements intended to be useful in supporting common usage by stakeholders.

Users of this document may include consumers, developers, and evaluators of secure IT products.

ISO/IEC 15408-4:2022: Information security, cybersecurity and privacy protection — Evaluation criteria for IT security Part 4: Framework for the specification of evaluation methods and activities

This document provides a standardised framework for specifying objective, repeatable and reproducible evaluation methods and evaluation activities.

This document does not specify how to evaluate, adopt, or maintain evaluation methods and evaluation activities. These aspects are a matter for those originating the evaluation methods and evaluation activities in their particular area of interest.

ISO/AWI TS 25481 Governance for DAOs in Blockchain and DLT

This project will specify the governance model for DAOs regarding three facets; IN CHAIN FACET: infrastructure level, where DAO membership requires an entity to only run a node of the DLT network (e.g. join a permissioned DLT network). ON CHAIN FACET: transaction and smart contract level, where DAO membership requires an entity to interact with a specific smart contract, or send specific transactions (e.g. register with a particular smart contract). OFF CHAIN FACET: non DLT system level, where DAO membership requires an entity to perform additional non-DLT activities (e.g. protect data privacy). The three facets can be used individually or as a blend in any variation for completeness as the minimum governance.This project will specify the governance model for DAOs regarding three facets; IN CHAIN FACET: infrastructure level, where DAO membership requires an entity to only run a node of the DLT network (e.g. join a permissioned DLT network). ON CHAIN FACET: transaction and smart contract level, where DAO membership requires an entity to interact with a specific smart contract, or send specific transactions (e.g. register with a particular smart contract). OFF CHAIN FACET: non DLT system level, where DAO membership requires an entity to perform additional non-DLT activities (e.g. protect data privacy). The three facets can be used individually or as a blend in any variation for completeness as the minimum governance.

Cloud computing

Cloud computing. ISO/IEC 19944 is applicable primarily to cloud service providers, cloud service customers and cloud service users, but also to any person or organization involved in legal, policy, technical or other implications of data flows between devices and cloud services."

Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. While opportunities exist with Big Data, the data can overwhelm traditional technical approaches and the growth of data is outpacing scientific and technological advances in data analytics. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) worked to develop consensus on important fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework series of volumes. This volume, Volume 3, contains the original 51 Version 1 use cases gathered by the NBD-PWG Use Cases and Requirements Subgroup and the requirements generated from those use cases. The use cases are presented in their original and summarized form. Requirements, or challenges, were extracted from each use case, and then summarized over all the use cases. These generalized requirements were used in the development of the NIST Big Data Reference Architecture (NBDRA), which is presented in Volume 6. During the development of Version 2 of the NBDIF, the Use Cases and Requirements Subgroup and the Security and Privacy Subgroup identified the need for additional use cases to strengthen work of the NBD-PWG in Stage 3. The subgroup accepted additional use case submissions using the more detailed Use Case Template 2. The three additional use case submissions collected using Use Case Template 2 are presented and summarized in this volume.

This document summarizes interfaces that are instrumental for the interaction with Clouds, Containers, and High Performance Computing (HPC) systems to manage virtual clusters to support the NIST Big Data Reference Architecture (NBDRA). The REpresentational State Transfer (REST) paradigm is used to define these interfaces, allowing easy integration and adoption by a wide variety of frameworks. Big Data is a term used to describe extensive datasets, primarily in the characteristics of volume, variety, velocity, and/or variability. While opportunities exist with Big Data, the data characteristics can overwhelm traditional technical approaches, and the growth of data is outpacing scientific and technological advances in data analytics. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework (NBDIF) series of volumes. This volume, Volume 8, uses the work performed by the NBD-PWG to identify objects instrumental for the NIST Big Data Reference Architecture (NBDRA) which is introduced in the NBDIF: Volume 6, Reference Architecture.

Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. While opportunities exist with Big Data, the data can overwhelm traditional technical approaches and the growth of data is outpacing scientific and technological advances in data analytics. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework series of volumes. This volume, Volume 6, summarizes the work performed by the NBD-PWG to characterize Big Data from an architecture perspective, presents the NIST Big Data Reference Architecture (NBDRA) conceptual model, and discusses the components and fabrics of the NBDRA.

Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. The growth of data is outpacing scientific and technological advances in data analytics. Opportunities exist with Big Data to address the volume, velocity and variety of data through new scalable architectures. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework (NBDIF) series of volumes. This volume, Volume 1, contains a definition of Big Data and related terms necessary to lay the groundwork for discussions surrounding Big Data.

The potential for organizations to capture value from Big Data improves every day as the pace of the Big Data revolution continues to increase, but the level of value captured by companies deploying Big Data initiatives has not been equivalent across all industries. Most companies are struggling to capture a small fraction of the available potential in Big Data initiatives. The healthcare and manufacturing industries, for example, have so far been less successful at taking advantage of data and analytics than other industries such as logistics and retail. Effective capture of value will likely require organizational investment in change management strategies that support transformation of the culture, and redesign of legacy processes. In some cases, the less-than-satisfying impacts of Big Data projects are not for lack of significant financial investments in new technology. It is common to find reports pointing to a shortage of technical talent as one of the largest barriers to undertaking projects, and this issue is expected to persist into the future. This volume explores the adoption of Big Data systems and barriers to adoption; factors in maturity of Big Data projects, organizations implementing those projects, and the Big Data technology market; considerations for implementation and modernization of Big Data systems; and, Big Data readiness.