Information technology — Cloud computing and distributed platforms — Data sharing agreement (DSA) framework

This document establishes a set of building blocks, i.e. concepts, terms, and definitions, including Data Level Objectives (DLOs) and Data Qualitative Objectives (DQOs), that can be used to create Data Sharing Agreements (DSAs). This document is applicable to DSAs where the data is intended to be processed using one or more cloud services or other distributed platforms.

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ISO/IEC 23751:2022 - Information technology — Cloud computing and distributed platforms — Data sharing agreement (DSA) framework Released:2/15/2022
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Information technology — Cloud
computing and distributed platforms
— Data sharing agreement (DSA)
Reference number
ISO/IEC 23751:2022(E)
© ISO/IEC 2022

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ISO/IEC 23751:2022(E)
© ISO/IEC 2022
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
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  © ISO/IEC 2022 – All rights reserved

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ISO/IEC 23751:2022(E)
Contents Page
Foreword .v
Introduction . vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Symbols and abbreviated terms.3
5 Overview of DSAs.3
5.1 General . 3
5.2 Data sharing scenarios . 4
5.3 Role of the DSA. 7
5.4 Trust as a key element in data sharing . 7
5.5 Data access and processing rights . 7
5.6 Data flow and DSA elements. 9
5.7 Relationship between data sharing and data portability . 10
5.8 Data sharing agreements (DSAs) in data lifecycles . 10
5.9 Data sharing agreements (DSAs) governance . 10
6 Dataset description .11
6.1 General . 11
6.2 DLOs and DQOs . 11
6.2.1 Title . 11
6.2.2 Domain . 11
6.2.3 Data dictionary . 11
6.2.4 Format. 11
6.2.5 Data types . 11
6.2.6 Data gathering policy . 11
6.2.7 Revision history . 11
6.2.8 Pre-existing transforms . 11
6.2.9 Date of the dataset . 11
6.2.10 Number of instances .12
6.2.11 Summary statistics .12
7 Data use obligations and controls .12
7.1 General .12
7.2 DLOs and DQOs . 13
7.2.1 Regulatory obligations and controls . 13
7.2.2 Data holder obligations and controls . 13
7.2.3 Allowed data uses . .13
7.2.4 Disallowed data uses . 14
7.2.5 Allowed uses of the data processing output . 14
7.2.6 Disallowed uses of the data processing output . 14
7.2.7 Data user obligations and controls . 14
8 Data provenance records, quality, and integrity .14
8.1 Data provenance records . 14
8.1.1 General . 14
8.1.2 DLOs and DQOs . 14
8.2 Data quality . 15
8.2.1 General .15
8.2.2 DLOs and DQOs .15
8.3 Integrity . 16
8.3.1 General . 16
8.3.2 DLOs and DQOs — Dataset integrity . 16
9 Chain of custody and transfer of custody .16
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ISO/IEC 23751:2022(E)
9.1 Chain of custody . 16
9.1.1 General . 16
9.1.2 DLOs and DQOs . 16
9.2 Transfer of custody . 17
9.2.1 General . 17
9.2.2 DLOs and DQOs . 17
10 Security and privacy .17
10.1 General . 17
10.2 DLOs and DQOs . 18
10.2.1 Data holder security requirements . 18
10.2.2 Data user security requirements . 18
10.2.3 Data holder privacy requirements . 18
10.2.4 Data user privacy requirements. 18
11 Proof of compliance .18
11.1 General . 18
11.2 DLOs and DQOs — Proof of compliance mechanisms . 19
Annex A (informative) Governance in ecosystems .20
Annex B (informative) Examples of alternatives to bespoke data sharing agreements (DSAs) .21
Annex C (informative) ISO/IEC standards for identity, privacy, chain of custody, forensics
and security.22
Bibliography .24
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ISO/IEC 23751:2022(E)
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
members of ISO or IEC participate in the development of International Standards through technical
committees established by the respective organization to deal with particular fields of technical
activity. ISO and IEC technical committees collaborate in fields of mutual interest. Other international
organizations, governmental and non-governmental, in liaison with ISO and IEC, also take part in the
The procedures used to develop this document and those intended for its further maintenance
are described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria
needed for the different types of document should be noted. This document was drafted in
accordance with the editorial rules of the ISO/IEC Directives, Part 2 (see or
Attention is drawn to the possibility that some of the elements of this document may be the subject
of patent rights. ISO and IEC shall not be held responsible for identifying any or all such patent
rights. Details of any patent rights identified during the development of the document will be in the
Introduction and/or on the ISO list of patent declarations received (see or the IEC
list of patent declarations received (see
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and
expressions related to conformity assessment, as well as information about ISO's adherence to
the World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT) see In the IEC, see
This document was prepared by Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 38, Cloud computing and distributed platforms.
Any feedback or questions on this document should be directed to the user’s national standards
body. A complete listing of these bodies can be found at and
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ISO/IEC 23751:2022(E)
For decades, organizations regarded data and its processing as an expense, necessary to business
operations but not an opportunity. What has changed recently is the realization of the value of data and
the added value that can potentially be generated by combining datasets. Artificial Intelligence (AI),
Big Data, analytics, and cloud computing are making this value proposition much more obvious and the
emergence of Internet of Things (IoT) is further driving the economic opportunities around data. Data
is the raw material for AI, a key component of the fourth industrial revolution.
Sharing datasets to create combined datasets can have several technical, business, and regulatory
challenges. One challenge is the lack of a common language to describe data sharing concepts across
the entire data lifecycle and the lack of guidance for developing data sharing agreements (DSAs). This
document offers standardized terminology for data sharing along with common building blocks that
can be used in the development of DSAs. The aim of the project is to reduce the time and cost required
to initiate data sharing projects.
Figure 1 illustrates the structure of this document, representing the Data Sharing Framework as
defining both Data Qualitative Objectives (DQOs) and Data Level Objectives (DLOs) over six distinct
aspects of data sharing. Each aspect is described in a separate section.
Figure 1 — Structure of this document
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Information technology — Cloud computing and
distributed platforms — Data sharing agreement (DSA)
1 Scope
This document establishes a set of building blocks, i.e. concepts, terms, and definitions, including Data
Level Objectives (DLOs) and Data Qualitative Objectives (DQOs), that can be used to create Data Sharing
Agreements (DSAs). This document is applicable to DSAs where the data is intended to be processed
using one or more cloud services or other distributed platforms.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
natural person or legal person, whether or not incorporated, or a group of either
[SOURCE: ISO/IEC 27729:2012,3.1]
data originator
party (3.1) that created the data and that can have rights
Note 1 to entry: A data originator can be an individual person.
Note 2 to entry: The data originator can be distinct from the natural or legal person(s) mentioned in, described
by, or implicitly or explicitly associated with the data. For example, PII can be collected by a data originator that
identifies other individuals. Those data subjects (PII Principals) can also have rights, in relation to the data set.
Note 3 to entry: Rights can include the right to publicity, right to display name, right to identity, right to prohibit
data use in a way that offends honourable mention.
data broker
party (3.1) that collects data from one or more sources and sells the data to one or more data users (3.5)
Note 1 to entry: In the context of data broker, sell means to provide data in exchange for money or other item of
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ISO/IEC 23751:2022(E)
data holder
party (3.1) that has legal control to authorize data processing (3.8) of the data by other parties
Note 1 to entry: A data originator (3.2) can be a data holder.
data user
party (3.1) that is authorized to perform processing of data under the legal control of a data holder (3.4)
chain of custody
demonstrable possession, movement, handling, and location of material from one point in time until
[SOURCE: ISO/IEC 27050-1:2016, 3.1]
data sharing
access to or processing of the same data by more than one authorized entity
Note 1 to entry: Use of the data can be synchronous or asynchronous.
Note 2 to entry: Data can be shared, for example, (i) by allowing access to, or the execution of operations over, the
original dataset, or (ii) by giving a copy of the data to the interested entity.
Note 3 to entry: The way in which data is shared fundamentally influences the available controls and the
statements needed in a data sharing agreement.
data processing
systematic performance of operations upon data
[SOURCE: ISO 2382:2015, 2121276, modified — Notes 1, 2, 3 and 4 to entry were deleted.]
cloud service agreement
documented agreement between the cloud service provider and cloud service customer that governs
the covered service(s)
Note 1 to entry: A cloud service agreement can consist of one or more parts recorded in one or more documents.
[SOURCE: ISO/IEC 19086-1:2016, 3.3]
data store
persistent repository for digital information
Note 1 to entry: A data store can be accessed by a single entity or shared by multiple entities via a network or
other connection.
[SOURCE: ISO/IEC 20924:2018, 3.1.13]
ratio scale
continuous scale with equal sized scale values and an absolute or natural zero point
[SOURCE: ISO 3534-2:2006, 1.1.9, modified — The EXAMPLE and Note 1 to entry were deleted.]
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ISO/IEC 23751:2022(E)
data level objective
commitment a data holder (3.4) or a data user (3.5) makes for a specific, quantitative characteristic of a
dataset, where the value follows the interval scale or ratio scale (3.11)
Note 1 to entry: A data level objective commitment can be expressed as a range.
data qualitative objective
commitment a data holder (3.4) or a data user (3.5) makes for a specific, qualitative characteristic of a
dataset, where the value follows the nominal scale or ordinal scale
Note 1 to entry: A data qualitative objective can be expressed as an enumerated list.
Note 2 to entry: Qualitative characteristics typically require human interpretation.
Note 3 to entry: The ordinal scale allows for existence/non-existence.
public domain data
class of data objects over which nobody holds or can hold copyright or other intellectual property
Note 1 to entry: Data can be in the public domain in some jurisdictions, while not in others.
Note 2 to entry: The concept of public domain and the difference between this and "publicly available" is subtle
and varies between jurisdictions. Readers should make themselves aware of the specific legal situation as it can
apply to them.
[SOURCE: ISO/IEC 19944-1:2020, 3.4.4]
4 Symbols and abbreviated terms
AI Artificial Intelligence
CSC Cloud Service Customer
CSP Cloud Service Provider
DLO Data Level Objective
DSA Data Sharing Agreement
DQO Data Qualitative Objective
5 Overview of DSAs
5.1 General
An emerging use of cloud services and other distributed platforms is the processing of data that the CSC
has acquired from a data holder. Additionally, there are cases where the CSC processes data acquired
from multiple data holders (multi-sourced data) and there are cases where two or more CSCs share
data among themselves including data acquired from other data holders.
Advances in cloud data storage have made it possible to create security boundaries around datasets that
are then part of a larger logical dataset. Some data repositories provide customized access privileges
to data users, with data provenance and chain of custody information attached to each record. These
can provide an alternative approach in data sharing scenarios where the data come from multiple,
independent data stores
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ISO/IEC 23751:2022(E)
5.2 Data sharing scenarios
A Data Sharing Agreement (DSA) can define how one or more organizations providing data to one or
more third parties, several organizations pooling information and making it available to each other or
to third parties. This document helps to identify and address important issues when developing DSAs
between two or more entities or individuals concerning the sharing of data or information of any kind
between these entities or individuals.
DSAs can be used in many different data sharing scenarios. Five representative scenarios are described
NOTE The arrows in the figures in this clause indicate data flow.
Figure 2 — Data sharing between two parties
Figure 2 shows a basic data sharing arrangement between a single data user and single data holder
using a DSA. The CSP is not a party to the DSA but rather the data user is a CSC using cloud services
provided by the CSP under a cloud service agreement.
The financial institution (Data Holder) clarifying to the financial institution bank teller (Data User) the DSA
applied. The financial institution bank teller (Data User) can likewise want to understand the cloud service
agreement with the CSP.
Figure 3 — Data sharing with one data user and multiple data holders
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ISO/IEC 23751:2022(E)
Figure 3 shows a data sharing arrangement between a single data user and multiple data holders. In
this scenario the data user has a DSA with each data holder. As with the scenario in Figure 2, the CSC
and CSP operate under a cloud service agreement and the CSP is not a party to the DSA.
An insurance broker (Data User) has a relationship with three insurance companies (Data Holders), with each
having unique DSAs. The insurance broker has a single cloud service agreement with their respective CSP.
Figure 4 — Data sharing between multiple data holders
Figure 4 shows a scenario where two or more data holders share data under a common DSA and then as
a group, they make use of cloud services from a CSP under a cloud service agreement.
A group of government agencies (Data Holders) have a mutually agreed upon DSA and have a common cloud
service agreement with a CSP.
The data sharing scenarios in Figures 3 and 4 include the issues of multi-sourced data which are
described in ISO/IEC TR 23186.
NOTE Entity does not include natural persons.
Figure 5 — Data sharing between departments within the same organization
Figure 5 shows data sharing between departments within the same entity where the sharing can be
governed by one or more policies rather than by a contractual agreement. In some jurisdictions, it can
be necessary to have a signed agreement between the data holder and the data user even if they are
within the same entity. For the purposes of this document, data sharing policies can include the same
elements of trust as DSAs.
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ISO/IEC 23751:2022(E)
A single financial institution offers banking and insurance from two distinct lines of business where they need
clarity by means of either policies or agreements or both to govern the permitted data sharing from one line of
business (Data Holder) to a Data User (such as Customer Relationship Management) in another line of business.
Figure 6 — Data sharing in a multiple ecosystem
As shown in Figure 6, data sharing can include more than one ecosystem. Ecosystems refer to networks
of interconnected organisations which share infrastructures and services. Figure 6 displays two
ecosystems. The first ecosystem includes one data holder, one data user and one cloud service provider
(CSP). The second ecosystem includes the same data user working with another data holder and another
CSP. The following observations can be made:
— Some business stakeholders (e.g. a data user) can have to manage DSAs from different ecosystems.
— DSAs used in a given ecosystem often include common elements, for instance.
— The introduction of policies established through a specific ecosystem governance scheme.
— The use of common cybersecurity and protection controls based on shared cybersecurity and
privacy risk analysis.
A health application ecosystem and a financial application ecosystem have separate DSA with the same data user
(a data analytics company).
A more comprehensive data sharing ecosystem can contain any combination of the scenarios described,
e.g. modelling the data sharing implemented, rolled up for an overall government or corporate

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