Integrating ISO Records Management Standards with ISO/IEC 42001:2023

A Practitioner’s Guide for AI Systems Documentation

Executive Summary

Organizations are increasingly adopting ISO/IEC 42001:2023, the first international standard for AI Management Systems (AIMS), to ensure responsible AI governance across the AI lifecycle (a-lign.com). Implementing ISO 42001 can be challenging, but existing records management standards from ISO/TC 46/SC 11 provide a ready foundation to meet these requirements. ISO’s records and information governance standards – including ISO 30301 (Management Systems for Records), ISO 15489 (Records Management Principles), and ISO 18128 (Risk Assessment for Records) – align closely with the clause structure and objectives of ISO 42001. These standards enable organizations to embed transparency, accountability, and evidence-based decision-making into their AI systems through robust recordkeeping practices. For example, ISO 15489 emphasizes the creation and management of authentic, reliable, and usable records to support business and compliance needs (filecorp.co.nz), which directly supports ISO 42001’s focus on AI system traceability and accountability. Likewise, ISO 30301 provides a certifiable framework with leadership commitment, risk-based thinking, performance evaluation, and continual improvement, mirroring the management system approach of ISO 42001 (linkedin.com). By mapping each clause of ISO 42001 to the corresponding records management controls and guidelines, this white paper shows how records professionals can strengthen AI governance. In practice, using SC 11 standards will enhance AI documentation (for example, logging model training data, decisions, and outcomes as official records) and improve AI risk management (for example, assessing risks of missing or poor records). The following analysis offers a clause-by-clause translation of the technical mapping between ISO 42001 and SC 11 standards into practical guidance, supplemented by case studies. Records and information managers will learn how familiar principles, such as establishing recordkeeping policies, conducting records risk assessments, and ensuring proper records control, can be applied to AI systems to achieve responsible, auditable, and compliant AI operations.

Introduction

Artificial Intelligence is now central to business and government operations, bringing tremendous benefits as well as ethical, privacy, and security challenges (a-lign.com). In response, ISO and IEC released ISO/IEC 42001:2023, a management system standard that provides a structured framework for AI governance (a-lign.com). ISO 42001 defines requirements for establishing an AI management system (AIMS), covering everything from organizational context and leadership to risk management, operations, and continuous improvement, to ensure AI systems are developed and used responsibly (a-lign.com). Achieving compliance with ISO 42001 means demonstrating transparency, accountability, bias mitigation, safety, and privacy in AI processes. Crucially, each of these principles relies on managing information effectively: organizations must document their AI systems, retain evidence of decisions, and control records of model development and performance.

This is where ISO’s records management standards come in. The subcommittee ISO/TC 46/SC 11 (Archives/Records Management) has developed a suite of standards that define best practices for managing information and records throughout their lifecycle. Key among these are: ISO 15489-1:2016 – Records Management Concepts and Principles, which outlines how to create and maintain records that are authentic, reliable, and usable as evidence of activities (filecorp.co.nz); ISO 30301:2019 – Management Systems for Records (MSR), a requirements standard that embeds recordkeeping into organizational governance (analogous to ISO 9001 or ISO 27001, but focused on records) (linkedin.com); and ISO 18128:2024 – Assessment of Records Risks, which guides identifying and analyzing risks to records, record processes, and systems. These and related SC 11 standards (e.g., on metadata, retention, and functional requirements for record systems) collectively enable organizations to systematically control their information assets. Notably, ISO 30301 adopts the same high-level structure as ISO 42001 (clauses 4–10 for context, leadership, planning, etc.), requiring leadership support, risk management, documented information control, monitoring, and improvement in the records program (linkedin.com). In other words, ISO 30301 establishes a governance framework for records that aligns with any ISO management system, including AIMS. It “integrates document/records control with enterprise governance” and facilitates auditing and continuous improvement of information practices (linkedin.com), capabilities that are invaluable when governing complex AI systems. Recognizing this synergy, ISO/TC 46/SC 11 has even formed a dedicated working group on “Records Management for Artificial Intelligence.” This new group aims to ensure that records standards directly support AI governance needs (linkedin.com).

This white paper translates a detailed clause-by-clause mapping between ISO/IEC 42001 and the SC 11 records management standards into practical guidance for records and information professionals. The goal is to show, in a practitioner-friendly way, how each requirement of ISO 42001 can be enabled or enhanced by applying SC 11 principles and tools. We proceed through ISO 42001’s main clauses (4 through 10), explaining the intent of each and highlighting the corresponding records management strategies that help meet them. Along the way, we include illustrative case studies demonstrating how SC 11 standards can be applied in real-world AI system scenarios – for example, documenting an AI model’s development for accountability, or using records risk assessment to identify information-related risks in an AI project. By the end, it will be clear that robust records and information governance are a cornerstone of trustworthy AI. Equipping AI initiatives with strong recordkeeping not only facilitates ISO 42001 compliance but also builds the transparency and evidence base needed for ethical AI, regulatory compliance (e.g., fulfilling the EU AI Act’s transparency and accountability requirements), and stakeholder trust. Records professionals have a critical role to play in this interdisciplinary effort, ensuring that the “memory” of AI systems – their data, decisions, and rationale – is securely captured and managed for the life of the system and beyond.

Structure of this White Paper: The next section provides a clause-level analysis, mapping each clause of ISO 42001 to relevant ISO/TC 46/SC 11 standards and illustrating how to implement the requirements in practice. Following the analysis, short case studies demonstrate practical applications of records management in AI system documentation and risk governance. The paper concludes with key takeaways and future outlook, emphasizing the value of integrating records management into AI management systems to achieve responsible, auditable AI. All citations and references are provided in Modern Public Administration (MPA) style for further reading.