ISO/IEC 23053:2022

International (ISO/IEC JTC 1/SC 42)

v2022

3 domains

21 controls

Descriptive framework and shared terminology for AI systems that use machine learning. It sets no certifiable requirements and no certification scheme exists against it. The units are descriptions of system components, approaches and pipeline stages, not duties.

Verified

ISO/IEC 23053:2022 is a compliance framework from International (ISO/IEC JTC 1/SC 42) with 3 domains and 21 controls. The largest domains are Machine learning pipeline – ISO/IEC 23053:2022 (8 controls), Machine learning approaches – ISO/IEC 23053:2022 (7 controls), Machine learning system – ISO/IEC 23053:2022 (6 controls). Every control below carries what it requires and what an assessor expects to see.

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Framework Domains (3)

Machine learning approaches – ISO/IEC 23053:2022

7 controls

Controls in the Machine learning approaches – ISO/IEC 23053:2022 domain of ISO/IEC 23053:2022: 7 controls
Code	Title	Description
iso-iec-23053-2022::7.1	General	The clause opens the approaches group by linking the method to the data. Supervised learning needs labelled examples, un...
iso-iec-23053-2022::7.2	Supervised machine learning	The public contents places supervised learning first among the approaches, which reflects its role as the reference case...
iso-iec-23053-2022::7.3	Unsupervised machine learning	The approach works without a labelled answer. The model looks for groups, patterns or a compressed representation in the...
iso-iec-23053-2022::7.4	Semi-supervised machine learning	The approach sits between supervised and unsupervised learning. A little supervision anchors the model, and the unlabell...
iso-iec-23053-2022::7.5	Self-supervised machine learning	The approach uses the structure of the data as the training signal rather than a separate label. A model can be asked to...
iso-iec-23053-2022::7.6	Reinforcement machine learning	The approach is defined by feedback rather than by examples: an agent acts, the environment responds, and a reward shape...
iso-iec-23053-2022::7.7	Transfer learning	The clause covers the reuse of a model that was already trained. The base model carries patterns from its original data,...

Machine learning pipeline – ISO/IEC 23053:2022

8 controls

Controls in the Machine learning pipeline – ISO/IEC 23053:2022 domain of ISO/IEC 23053:2022: 8 controls
Code	Title	Description
iso-iec-23053-2022::8.1	General	The clause introduces the pipeline that the rest of clause 8 describes. Before the pipeline starts, the task must be def...
iso-iec-23053-2022::8.2	Data acquisition	The clause treats acquisition as the first pipeline stage. Training and evaluation both depend on the data, so it must s...
iso-iec-23053-2022::8.3	Data preparation	The clause says data is rarely ready for training and that preparation can take a large share of the effort. It lists th...
iso-iec-23053-2022::8.4	Modelling	The clause describes the step that produces a trained and tested model ready for deployment. It covers feature engineeri...
iso-iec-23053-2022::8.5	Verification and validation	The clause separates two questions. Model evaluation compares predictions on test data with the actual labels and report...
iso-iec-23053-2022::8.6	Model deployment	The clause covers putting a trained and evaluated model into an operating environment where it can serve production data...
iso-iec-23053-2022::8.7	Operation	The clause follows deployment. The model is available as a stand-alone application or as a function inside another syste...
iso-iec-23053-2022::8.8	Example machine learning process based on the pipeline	The public contents gives this clause its own page and title. It uses the pipeline to show an example machine learning p...

Machine learning system – ISO/IEC 23053:2022

6 controls

Controls in the Machine learning system – ISO/IEC 23053:2022 domain of ISO/IEC 23053:2022: 6 controls
Code	Title	Description
iso-iec-23053-2022::6.1	Overview of the machine learning system	The clause describes the elements of an ML system and the roles around them, noting that the functions can sit with diff...
iso-iec-23053-2022::6.2	Task	The clause explains that a task identifies the problem to be solved with the model, that a system can have more than one...
iso-iec-23053-2022::6.3	Model	The clause defines an ML model as a data structure and the software that processes it, both fixed by the chosen algorith...
iso-iec-23053-2022::6.4	Data	The public contents gives this clause one page and no titled subclauses. The framework treats data as one of the three c...
iso-iec-23053-2022::6.5	Tools	This is the largest technical component in the framework. The public contents gives it five titled subclauses: general,...
iso-iec-23053-2022::6.5.3	Categories of machine learning algorithms	The public contents places the categories of ML algorithms under the tools component, between data preparation and optim...

What is ISO/IEC 23053:2022 and who does it apply to?

ISO/IEC 23053:2022 is a compliance framework from International (ISO/IEC JTC 1/SC 42) with 3 domains and 21 controls. Descriptive framework and shared terminology for AI systems that use machine learning. It sets no certifiable requirements and no certification scheme exists against it. The units are descriptions of system components, approaches and pipeline stages, not duties. It is used by organisations to establish and maintain compliance with industry standards and regulatory requirements.

What does ISO/IEC 23053:2022 actually require?

ISO/IEC 23053:2022 has 21 controls organised across 3 domains. The largest domains are Machine learning pipeline – ISO/IEC 23053:2022 (8 controls), Machine learning approaches – ISO/IEC 23053:2022 (7 controls), Machine learning system – ISO/IEC 23053:2022 (6 controls). Each control defines specific requirements that organisations must implement to achieve compliance.

If I already comply with another framework, how much of ISO/IEC 23053:2022 do I already cover?

ISO/IEC 23053:2022 does not currently have cross-framework mappings in our system. Check back as we continuously expand our mapping database.

How do I implement ISO/IEC 23053:2022?

Start your ISO/IEC 23053:2022 compliance journey by running a self-assessment on our platform to identify your current compliance posture. Our AI advisory can answer specific questions about ISO/IEC 23053:2022 requirements, and cross-framework mapping helps you leverage existing controls from other frameworks you may already comply with. Create a free account to access all 21 controls and track your progress.

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