Information technology — Governance of data — Part 3: Guidelines for data classification

This document provides essential guidance for members of governing bodies of organizations and management on the use of data classification as a means to support the organization’s overall data governance policy and associated systems. It sets out important factors to be considered in developing and deploying a data classification system.

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Information technology — Governance
of data —
Part 3:
Guidelines for data classification
Technologies de l'information — Gouvernance des technologies de
l'information —
Reference number
ISO/IEC TS 38505-3:2021(E)
© ISO/IEC 2021

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ISO/IEC TS 38505-3:2021(E)
© ISO/IEC 2021
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
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ISO/IEC TS 38505-3:2021(E)
Contents Page
Foreword .v
Introduction . vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Foundations . 4
4.1 Context . 4
4.1.1 The data deluge . 4
4.1.2 The strategic value of data . 4
4.1.3 The risks associated with data . 4
4.1.4 Consequences of failure . 4
4.2 Data classification. 5
4.3 Purpose of classification: . 5
4.4 Engage and empower staff . . 6
4.5 Structure of this document . 6
5 Roles and responsibilities . 6
5.1 General . 6
5.2 Role of governing body . 8
5.2.1 General . 8
5.2.2 Understanding the role of data . 8
5.2.3 Governance of data . 8
5.2.4 Data classification approach . 8
5.2.5 Data classification and risk management . 8
5.2.6 Direct according to policy . 9
5.2.7 Monitor conformance and performance . 9
5.3 Role of management . . 9
5.3.1 General . 9
5.3.2 Setting the scope of data classification . 9
5.3.3 Propagating and implementing policy . 9
5.3.4 Defining roles and responsibilities . 10
5.3.5 Mobilizing the organization in support of the policy. 10
5.3.6 Operation . 11
5.3.7 Feedback from management to the governing body . 11
5.3.8 Levels, discovery and attribution . 11
5.4 Changing classifications. 11
5.5 Defining the requirements: key considerations .12
6 Data classification framework .12
6.1 Context . 12
6.2 Identification . 13
6.3 Implementation . .13
6.4 Monitor/Improve . 14
7 Guiding principles .14
7.1 Simplicity . 14
7.2 Default classifications . 14
7.3 Interoperability. 14
7.4 Equivalence. 14
7.5 Use of data classification for processor and controller . 15
7.6 Auditing, controls and compliance . 15
7.7 Customer data . 15
7.8 Assessment and reporting . 16
7.9 Learning, maintaining and improving . 16
7.10 Data protection . 16
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ISO/IEC TS 38505-3:2021(E)
Bibliography .17
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ISO/IEC TS 38505-3:2021(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 Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 40, IT Service Management and IT Governance.
A list of all parts in the ISO/IEC 38505 series can be found on the ISO and IEC websites.
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 TS 38505-3:2021(E)
This document complements the existing International Standards on IT governance (ISO/IEC 38500)
and data governance (ISO/IEC 38505-1). It is designed to provide practical guidance for organizations
including governing bodies and management to allow them to:
— maintain an oversight of their data portfolio,
— understand the business context, value, sensitivity and risk associated with the data, and
— apply mechanisms that are both proportionate and appropriate, ensuring that data is protected,
and is only used for intended purposes consistent with the organization’s obligations.
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Information technology — Governance of data —
Part 3:
Guidelines for data classification
1 Scope
This document provides essential guidance for members of governing bodies of organizations and
management on the use of data classification as a means to support the organization’s overall data
governance policy and associated systems. It sets out important factors to be considered in developing
and deploying a data classification system.
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/
big data
extensive datasets, primarily in the data characteristics of volume, variety, velocity and/or variability,
that require scalable technology for efficient storage, manipulation, management and analysis
Note 1 to entry: Big data is commonly used in many different ways, for example as the name of the scalable
technology used to handle big data extensive datasets.
[SOURCE: ISO/IEC 20546:2019, 3.1.2, modified.]
customer data
data held on file about customers
Note 1 to entry: This comprises the information customers provide while interacting with the organization via
their website, mobile applications, surveys, social media, marketing campaigns and other online and offline
data controller
person or organization who determines the purposes for which and the manner in which any data are
to be processed, stored and used
[SOURCE: ISO 10667-1:2020, 3.10, modified.]
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data processor
person [other than an employee of the data controller (3.3)] or organization that processes the data on
behalf of the data controller.
[SOURCE: ISO 10667-1:2020, 3.11, modified.]
data quality owner
senior level employee accountable for the quality of one or more datasets
data sensitivity
property of data that reflects the potential harm of unauthorized disclosure
Note 1 to entry: The potential harm is to an individual or organization.
Note 2 to entry: Different levels of data protection can be used to account for varying levels of data sensitivity.
Note 3 to entry: Data sensitivity can be applied to specific categories of data such as healthcare, finance, personal
data (3.12)
data sharing
access to or processing of the same data by more than one authorized entity
data stakeholder
natural or legal person that can affect, be affected by, or perceive themselves to be affected by a decision
or activity related to the processing of data
data steward
role within an organization responsible for ensuring that data-related work is performed according to
policies and practices as established through data governance
Note 1 to entry: Typically, data stewards are responsible for business controls, data content and meta-data
management related to a set of data assets, utilizing an organization's data governance processes to ensure
fitness of data elements, both the content and metadata.
[SOURCE: ISO/TR 14872:2019, 3.5, modified.]
data taxonomy
scheme for organizing data based upon relationships and common characteristics
Note 1 to entry: A data taxonomy can include data categorization and data classification; it represents a
convenient way to organize data.
organizational data
class of data objects under the control, by legal, contractual or other reasons, of an organization.
Note 1 to entry: Organizational data are all business information and data that are accessed, collected, used,
processed, stored, shared, distributed, transferred, disclosed, destroyed or disposed of by any of the business
units. Organization data can include, for example, financial records, business strategy documents, governing
body and governance papers, staff information (employees, contractors, consultants), business analysis and
intelligence, and executive information.
Note 2 to entry: Organizational protected data, or OPD, is organizational data for which protection is required
based on the policies established by the governance of data process.
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Note 2 to entry: Organizations have policies that govern the data under their control. ISO/IEC 38505-1 identifies
and examines higher level governance concerns regarding the use of data which is relevant from the perspective
of governance of data.
Note 3 to entry: Organizational data can contain OPD and PII.
[SOURCE: ISO/IEC 19944-1:2020, 3.4.2, modified.]
personally identifiable information
personal data
any information that (a) can be used to establish a link between the information and the natural person
to whom such information relates, or (b) is or can be directly or indirectly linked to a natural person.
Note 1 to entry: The “natural person” in the definition is the PII principal (3.13). To determine whether a PII
principal is identifiable, account should be taken of all the means which can reasonably be used by the privacy
stakeholder holding the data, or by any other party, to establish the link between the set of PII and the natural
[SOURCE: ISO/IEC 29100:2011/Amd1: 2018, 2.9, modified.]
PII principal
natural person to whom the personally identifiable information (PII) (3.12) relates
Note 1 to entry: Depending on the jurisdiction and the particular PII protection and privacy legislation, the
synonym “data subject” can also be used instead of the term “PII principal”.
[SOURCE: ISO/IEC 29100:2011, 2.11]
semi-structured data
aggregate datatype whose components' datatypes and their labels are not pre-determined
Note 1 to entry: Semi-structured data are forms of structured data (3.15) that do not follow the formal structure
of data models related to relational databases or other forms of databases.
Note 2 to entry: Examples of semi-structured data include the data that contain HTML tags or other markers to
separate semantic elements and to represent hierarchies of records and fields within the data.
[SOURCE: ISO/IEC 20944-1:2013,, modified.]
Note 3 to entry:
structured data
data which are organized based on a pre-defined (applicable) set of rules.
Note 1 to entry: The predefined set of rules governing the basis on which the data are structured needs to be
clearly stated and made known.
Note 2 to entry: A pre-defined data model is often used to govern the structuring of data.
Note 3 to entry: Examples of structured data are the data contained in relational databases.
[SOURCE: ISO/IEC 20546:2019, 3.1.35, modified.]
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4 Foundations
4.1 Context
4.1.1 The data deluge
As organizations create, process and share ever more data, they run the risk of being overwhelmed by a
data deluge. Due to rapid growth in global data volumes, any attempted quantification risks becoming
quickly obsolete; nevertheless, some indications are available.
The World Economic Forum (WEF) has estimated that the global volume of data will double between
2018 and 2022 and with then double again within 3 years. Currently, much of this growth is driven by
searches (5 billion per day) and social media platforms (Twitter: 456 thousand tweets every minute),
while future growth is expected to be driven mainly by new data scenarios, especially those related to
big data, artificial intelligence (AI) and Internet of Things (IoT).
As data has proliferated, it has become a key enabler for the effective operations of all organizations
and critical for effective decision-making by both managers and governing bodies. The pervasiveness
of data in organizations today mandates the governance of data as an organizational imperative.
As a consequence, managers and governing body members should seek to better acquaint themselves
with the potential value, risk and constraints associated with data.
4.1.2 The strategic value of data
More and more organizations understand that data constitutes a strategic asset which has financial and
non-financial value, and which can be used in turn to generate additional value for the organization.
Hence the focus on enabling organizations to leverage the value of their data without incurring data/
privacy breaches, unethical use, disclosing intellectual property, or having its data misappropriated or
Each organization should consider the data opportunity relative to its strategic context, the nature of
the data in its custody, and the risk appetite as defined by the organization’s governing body.
4.1.3 The risks associated with data
While data presents an organization with strategic, value-generating opportunities, it can also pose
significant threats. Data can be exposed to inappropriate or illegal access and used for illegal purposes.
It can be lost and, as a result, expose natural and legal persons to threats against them. It can even be
used against the organization itself in anti-competitive, unethical, or illegal ways.
Each organization should consider the threats posed by data in its care, assess the risks and take steps
to appropriately address these risks.
4.1.4 Consequences of failure
Ineffective data stewardship by the organization can present very real threats to the organization.
Examples include:
a) Data breach litigation: with potentially significant penalties, including legal prosecution and
financial penalties. The full cost of a data breach can last for months or even years.
b) Critical failure: disclosure of information, for example via a data breach, that can result in financial
loss, the failure of critical infrastructure or, in the most serious cases, to loss of life.
c) Reputational risk: reputation matters in many ways, for example, the ability to recruit top talent or
to retain the trust of customers, regulators, investors and other stakeholders.
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d) Under-performance: poor data stewardship by the organization can lead to under-performance of
the organization and put it at a disadvantage relative to peers or competitors.
4.2 Data classification
There are many ways to organize data. A data taxonomy organizes data into various groups or
hierarchies based on a desired facet of data. For example, data can be organized into sub-groups based
on the nature of what the underlying data describes (data categories), or based on geo-location of data,
or the level of de-identification performed on the data, or on the legal means of control over the data.
Another important facet of data is its level of classification. The classification level describes the
significance or sensitivity of the data, from the perspective of an organization. For example, it can be
described as N levels of significance (N being scenario-specific). Data sensitivity, and its associated
degree or level, is determined by the purpose and context of the organization and relates to the
potential value, risk and constraints of the data. Different classifications allow the organization to have
differentiated policies and associated controls and costs based on the data’s significance. This ensures
each class of data receives the appropriate treatment (e.g. level of security controls or compliance
See ISO/IEC 19944-1 for a description of a multi-faceted data taxonomy, where data classification
is one such facet. ISO/IEC 19944-1 describes how data classification is an important facet in an
otherwise broader, multi-faceted taxonomy of data. It is important to note the distinction between data
classification and data categorization; the latter refers more to the nature of the data. Some examples
that illustrate this distinction are shown in Table 1.
Table 1 — Distinguishing data classification and data categories
Data category: examples Data classification: examples
Customer data, e.g. identity data, descriptive data High business impact (HBI), medium business impact
(MBI), low business impact (LBI).
Aggregated data, e.g. summary statistics Confidential, restricted, internal use, public
Derived data, e.g. telemetry
Anonymized data, so that it is impossible to re-identify
an individual
Structured data, e.g. stored in a structure such as a
4.3 Purpose of classification:
Data classification provides a means for the organization to objectively distinguish between different
datasets, so as to indicate the significance of the data and to allow differentiated policies and controls
consistent with the significance of the data and with its compliance obligations. Data classification is a
fundamental requirement for many effective data stewardship activities in an organization.
Some examples of policies and controls that can be applied differently to different classification levels
a) Data Protection: This ensures that a risk-based approach is taken for each different classification
of data such that treatments are appropriate, cost effective and enable the achievement of the
organization’s strategic objectives.
b) Compliance: Applying the correct compliance processes for each classification helps to ensure the
policies and controls for that level are correctly implemented.
c) Intended use: A core tenet of data stewardship is that data is used only for legitimate and agreed
purposes. That means that data can be used or processed only for the purpose explicitly stipulated
in an agreement with, and under the parameters defined in, the data agreement. Data classification
provides a useful mechanism that ensures that definitive and unambiguous details are assigned to
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data attributes such that they are easily understood by people and systems with which they come
into contact.
d) Data quality: Data classification (along with data categorization) can help to ensure the necessary
treatment of that data to confirm that the appropriate level of data quality is maintained.
e) Innovation: Given the rapidly evolving data landscape, with emerging data scenarios, it is important
that the organization considers innovation in its data classification schemes. For example, Internet
of Things, artificial intelligence and big data scenarios could test established policies and controls
of data assets by the organization.
4.4 Engage and empower staff
Whether through their action or inaction, understanding or misunderstanding, staff of the organization
exert a power

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