The CREST Accreditation Standards set requirements for organisations that provide cyber security services, verified through independent assessment. They are published by CREST, an international not-for-profit body. Three components address AI. Domain 7 requires all accredited organisations to govern their use of AI, including validating AI-supported outputs. Annex B sets requirements for using AI in penetration testing, with human validation of findings. A third standard covers security testing of generative AI and large language model-enabled systems.

IEEE 3198-2025 is a technical standard that specifies a method for evaluating the fairness of machine learning systems. It was published by the IEEE Standards Association in May 2025. The standard categorises the causes of unfairness in machine learning and presents widely used definitions of fairness. For each definition, it specifies metrics and explains how to calculate them. Test cases set out detailed procedures for fairness evaluations. This supports consistent, repeatable fairness testing across systems and organisations.

An open specification and command line interface (CLI) toolkit for cryptographically pre-registering machine learning evaluation criteria, including the metric, threshold, dataset, and seed, before a model is run. By committing these criteria in advance, the tool makes any post hoc changes to success thresholds detectable rather than silent, supporting greater integrity and accountability in reported ML evaluation claims.

A three-layer, enterprise-wide AI risk management and governance framework that operationalises trustworthy AI from board strategy to operational controls and organisational resilience.

Content Credentials provide tamper-evident metadata that gives you more information about how a piece of digital content was created and edited.

VERA-MH (Validation of Ethical and Responsible AI in Mental Health) is a comprehensive framework for evaluating AI chatbots in a mental health context.

The Approved Intelligence Platform (AIP) provides modular, scenario-based testing workflows to evaluate mission-critical AI systems in defence, public safety, and critical civil use cases. It delivers a comprehensive, end-to-end testing environment based on a proprietary AI trust ontology with measurable AI Solutions Quality Indicators (ASQI) for the testing, evaluation, validation and verification of software solutions with different AI modalities.

MISSION KI is developing a voluntary quality standard guideline for artificial intelligence (AI) that strengthens the reliability and trustworthiness of AI applications and systems. It sets a voluntary, evidence-based self-assessment framework for AI providers below the EU AI Act’s high-risk threshold. It defines six quality dimensions (data governance, non-discrimination, transparency, human oversight, reliability, AI-specific cybersecurity) and a stepwise procedure: describe the use case, analyse protection needs, rate requirements via a VCIO catalogue, document tests/evidence, validate findings, issue a report, and monitor validity.

Croissant is an open-source framework developed by MLCommons to standardise dataset descriptions, enhance data discoverability, and facilitate automated use across machine-learning tasks. Croissant ensures datasets are consistently documented by providing structured metadata schemas, improving interoperability, transparency, and ease of integration.

This document addresses how artificial intelligence machine learning can impact the safety of machinery and machinery systems.

ISO/IEC 25023:2016 defines quality measures for quantitatively evaluating system and software product quality in terms of characteristics and subcharacteristics defined in ISO/IEC 25010 and is intended to be used together with ISO/IEC 25010.

Defining quality measures for quantitatively evaluating system and software product quality in terms of characteristics and subcharacteristics defined in ISO/IEC 25010 and is intended to be used together with ISO/IEC 25010.

The MPAI AI Framework (MPAI-AIF) Technical Specification specifies architecture, interfaces, protocols and Application Programming Interfaces (API) of an AI Framework (AIF), especially designed for execution of AI-based implementations, but also suitable for mixed AI and traditional data processing workflows.

MPAI-CAE V1.4 is a collection of four use cases to improve the user audio experience in a variety of situations.

This standard adopts MPAI Technical Specification Version 1.2 as an IEEE Standard.

This Recommendation specifies an architectural framework for network automation based on artificial intelligence (AI) for resource and fault management in future networks, including international mobile telecommunications-2020 (IMT-2020).

Recommendation ITU-T Y.3172 specifies an architectural framework for machine learning (ML) in future networks including IMT-2020.

This Recommendation provides an architectural framework for machine learning (ML) models serving in future networks including IMT-2020, i.

This DIN SPEC in accordance with the PAS procedure has been drawn up by a DIN SPEC (PAS)-consortium set up on a temporary basis.

The DIN SPEC series describes a number of AI quality requirements which are structured using an AI quality meta model. The DIN SPEC series applies to all phases of the life cycle of an AI module.