AI Governance

ISO/IEC 23659:2024 (AI Risk Mgmt)

Adhering to ISO/IEC standards during software migrations is critical for ensuring quality, reliability, and compliance. These standards provide a framework that helps organizations mitigate risks, maintain data integrity, and build stakeholder confidence. By implementing best practices and utilizing the right tools, teams can successfully navigate the complexities of migration projects while adhering to these crucial guidelines.

by ISO/IEC Joint Technical Committee

NIST AI RMF 1.0

Integrating NIST standards into your software migration projects ensures a secure and compliant transition from legacy systems. By focusing on security, regulatory adherence, and best practices, you can safeguard data integrity and build trust among stakeholders, making your migration efforts more efficient and reliable.

by National Institute of Standards and Technology

EU AI Act (Regulation (EU) 2024/1689)

The EU's horizontal regulation for artificial intelligence, classifying systems by risk and attaching obligations to each tier. It entered into force on 1 August 2024; prohibited practices and AI literacy applied from 2 February 2025, general-purpose AI obligations from 2 August 2025, and Article 50 transparency obligations from 2 August 2026. The Digital Omnibus on AI, approved by the Council on 29 June 2026, deferred stand-alone high-risk (Annex III) obligations to 2 December 2027 and high-risk AI embedded in regulated products (Annex I) to 2 August 2028.

by European Union

IEEE 7001-2021 (AI Transparency)

Adhering to IEEE standards during software migrations is crucial for minimizing risks and ensuring system reliability. By understanding key requirements, implementing effective processes, and utilizing appropriate tools, teams can enhance collaboration and boost confidence in their migration strategies. This structured approach leads to smoother transitions and successful outcomes.

by Institute of Electrical and Electronics Engineers

IEEE 7002-2022 (AI Privacy Data)

Adhering to IEEE standards during software migrations is essential for minimizing risks, enhancing communication, and ensuring regulatory compliance. This comprehensive guide outlines key requirements, practical steps for compliance, and tools to facilitate successful migration projects while addressing common challenges that teams may face.

by Institute of Electrical and Electronics Engineers

ISO/IEC 22989:2022 (AI Concepts)

Adhering to ISO/IEC standards during software migrations is essential for ensuring data integrity, security, and stakeholder confidence. This guide outlines key requirements, compliance considerations, and practical steps to ensure your migrations align with these globally recognized standards, helping you mitigate risks and achieve successful outcomes.

by ISO/IEC Joint Technical Committee

ISO/IEC 23053:2022 (AI Lifecycle)

Adhering to ISO/IEC standards during software migrations is essential for ensuring data integrity, security, and operational efficiency. By implementing thorough documentation, quality assurance checks, and leveraging appropriate tools, teams can effectively manage migration risks and enhance stakeholder confidence.

by ISO/IEC Joint Technical Committee

ISO/IEC 42001:2023 (AI management system)

Management system requirements for establishing, implementing, and continually improving an AI management system (AIMS).

by ISO/IEC

NIST AI Risk Management Framework 1.0

Guidelines to integrate trustworthiness considerations into the design, development, and deployment of AI systems.

EU AI Act (Political Agreement)

First comprehensive regulatory framework for trustworthy AI in the European Union.

Google Responsible AI Principles

Seven commitments guiding the ethical development and deployment of AI at Google.

Microsoft Responsible AI Standard v2

Company-wide governance framework translating principles into measurable requirements.

OpenAI Safety & Alignment Best Practices

Mitigation strategies (RLHF, red-teaming, tiered access) for large language model deployment.

ISO/IEC 42001 AI Management System

ISO/IEC 42001 is the first international standard for an Artificial Intelligence Management System, giving organizations a certifiable framework to govern AI responsibly.

AI TRiSM (Trust, Risk and Security Management)

AI TRiSM is a framework for managing the trust, risk, and security of AI systems across explainability, model operations, data protection, and runtime application security.

Responsible-AI Review Checklist

Governance verification items for assessing fairness, transparency, accountability, and risk before deploying an AI system.

What is MLOps?

MLOps is a set of practices for reliably building, deploying, monitoring, and maintaining machine learning systems in production, applying DevOps principles to the ML lifecycle. It covers data and model versioning, automated training and evaluation pipelines, deployment, and ongoing monitoring for drift and performance decay. The goal is reproducible, auditable models that can be retrained and rolled out safely as data and requirements change.

What is responsible AI?

Responsible AI is the practice of designing, building, and operating AI systems so they are fair, transparent, accountable, secure, and respectful of privacy. It addresses risks such as biased outputs, lack of explainability, misuse, and harm to individuals, often guided by frameworks like the NIST AI Risk Management Framework or the EU AI Act. In practice it combines governance policies, bias and safety testing, human oversight, and ongoing monitoring throughout the model lifecycle.