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.

The AI Risk Ontology (AIRO) is an open-source formal ontology that provides a minimal set of concepts and relations for modelling AI use cases and their associated risks. AIRO has been developed according to the requirements of the EU AI Act and international standards, including ISO/IEC 23894 on AI risk management and ISO 31000 family of standards.

Vectice is a regulatory MLOps platform for AI/ML developers and validators that streamlines documentation, governance, and collaborative reviewing of AI/ML models. Designed to enhance audit readiness and ensure regulatory compliance, Vectice automates model documentation, from development to validation. With features like automated lineage tracking and documentation co-pilot, Vectice empowers AI/ML developers and validators to work in their favorite environment while focusing on impactful work, accelerating productivity, and reducing risk.

Enzai’s EU AI Act Compliance Framework makes achieving compliance with the world’s first comprehensive AI governance legislation as easy as possible. The framework breaks hundreds of pages of regulations down into easy-to-follow steps allowing teams to enable organisations to seamlessly and confidently assess the compliance of their AI Systems with the Act and complete the requisite conformity assessments.

The Algorithmic Transparency Recording Standard (ATRS) is a framework for capturing information about algorithmic tools, including AI systems. It is designed to help public sector bodies openly publish information about the algorithmic tools they use in decision-making processes that affect members of the public.

Anekanta® AI’s AI Risk Intelligence System™ solves the problem of biometrics and AI system risk classification by challenging the entire AI system lifecycle and providing risk reports which inform compliance requirements in line with the EU AI Act and international regulatory frameworks.

AI/ML applications have unique security threats. Project GuardRail is a set of security and privacy requirements that AI/ML applications should meet during their design phase that serve as guardrails against these threats. These requirements help scope the threats such applications must be protected against.

Use Saidot to empower AI teams to do high-quality AI governance efficiently and with quality

Comprehensive AI governance and compliance platform focused on empowering organizations with the risk management capabilities they need to adopt AI swiftly and responsibly.

AI assurance as smart as your AI systems

This paper addresses the complex and contentious issues surrounding AI governance, focusing on organizing overarching and systematic approaches for the transparency of AI algorithms, and proposes a practical toolkit for implementation. Designed to be highly user-friendly for both businesses and government agencies engaging in self- and co-regulation, the toolkit presents disclosure items in a list format, allowing for discretion and flexibility when considering AI algorithm providers, users, and risks.

This app matches information in model cards to proposed regulatory compliance descriptions in the EU AI Act. This is a prototype to explore the feasibility of automatic checks for compliance, and is limited to specific provisions of Article 13 of the Act, “Transparency and provision of information to users”.

FRAIA assesses the risks to human rights posed by algorithms and promotes measures to address them. It fosters dialogue among professionals involved in algorithm development or deployment. The client is accountable for implementing FRAIA to prevent uncertain outcomes of algorithm use. FRAIA mitigates risks of carelessness, ineffectiveness, or violation of citizens' rights.

QLECTOR LEAP is a cloud-based application that helps guide production processes in the same way the GPS supports your road trip. It uses historical data and AI to forecast production and suggest optimal measures when unplanned events occur. It combines machine learning methods specific for manufacturing and uses own AI technology to build and maintain data-driven digital twin of a factory.

Enterprise platform for governance, risk and compliance in AI. Regulatory compliance with global laws intersecting with AI.

GRACE is an AI governance platform that offers a central registry for all AI models, tools, and workflows for risk mitigation, compliance, and collaboration across AI teams and projects. It provides real-time compliance, transparency, and trust while promoting AI innovation. The platform works across all major cloud vendors and offers out-of-the-box frameworks for complying with EU AI Act, AI standards, data regulations, and AI frameworks.

Automated AI testing, monitoring, and governance

An accessible framework (single A3 sheet) for governing the ethical development and use of AI - including the what (ethic), how (realisation principals), and submission of evidence for approval. Trustworthiness and bias are also covered

Zupervise is a unified risk transparency platform for AI governance.

AI governance, risk, and compliance software platform that helps organizations understand and manage the risks associated with AI, while meeting their evolving regulatory obligations