Artificial intelligence is no longer experimental in regulated industries. It is being used to monitor claims, scan clinical literature, validate labels, screen ingredient risks, predict quality trends, and surface early safety signals.
The opportunity is significant. But so is the exposure.
As AI systems increasingly influence compliance-sensitive decisions, organizations are shifting from asking “What can AI do?” to “How do we govern it responsibly?”
ISO/IEC 42001:2023 is the first international management system standard developed specifically for Artificial Intelligence Management Systems (AIMS). It provides a structured framework for governing AI across its lifecycle — from initial scoping and development to deployment, monitoring, and retirement.
ISO/IEC 42001:2023 is a management system standard — similar in structure to ISO 9001 (quality) or ISO 27001 (information security) — but focused specifically on AI governance.
Rather than prescribing technical architecture, it establishes requirements for:
In short, it treats AI as an organizational system that must be governed — not just a technology to be deployed.
The standard is risk-based. Organizations are required to assess AI risks in their specific context and implement appropriate controls. Annex A of the standard includes 38 reference controls that can be selected based on relevance and documented in a Statement of Applicability.
This flexibility is important. A chatbot used for internal summarization carries a very different risk profile than an AI tool influencing clinical, labelling, or safety decisions.
One of the most important aspects of ISO/IEC 42001 is its lifecycle orientation.
AI governance does not begin at launch — and it does not end there.
Governance should extend across stages such as:
AI systems evolve. Data changes. Models drift. New risks emerge. Governance frameworks must therefore be dynamic rather than static.
This is particularly relevant in regulated sectors, where documentation, traceability, and change management are already expected in other systems such as quality management and pharmacovigilance programs.
Many organizations in regulated environments are already deploying AI in ways that directly intersect with compliance expectations, including:
These systems may rely on third-party models, evolving datasets, and automated decision pathways that are not always transparent during inspection or audit.
ISO/IEC 42001 does not replace regulatory obligations. However, it provides a formal governance structure that can complement existing compliance frameworks by strengthening:
For industries such as:
AI governance is increasingly becoming part of broader quality and risk conversations.
AI governance is often discussed in terms of ethical AI, responsible AI, or lawful AI. ISO/IEC 42001 operationalizes those concepts.
It requires organizations to move beyond high-level principles and implement:
It also aligns well with other established risk management approaches, such as ISO 31000 and the NIST AI Risk Management Framework, allowing organizations to integrate AI risk into enterprise-level risk programs.
The result is not bureaucracy for its own sake. It is a repeatable framework that can support trust, defensibility, and scalability.
As AI becomes embedded in regulated decision-making — whether in claims review, labelling workflows, literature monitoring, quality analytics, or safety surveillance — governance expectations increasingly overlap with regulatory and quality system oversight.
AI does not operate outside existing compliance structures. It influences them.
For organizations operating in high-scrutiny sectors, this raises practical questions:
ISO/IEC 42001 introduces a structured way to formalize those expectations.
dicentra is closely monitoring the development and adoption of ISO/IEC 42001 and related AI governance frameworks to assess how they may integrate with existing regulatory, quality, and risk management systems across life sciences and consumer health industries.
ISO/IEC 42001:2023 represents an important step in the maturation of AI governance. It reframes responsible AI from a conceptual discussion into a structured, auditable management system.
As AI continues to influence regulated functions — from claims and labelling to quality and safety monitoring — organizations may increasingly look for frameworks that demonstrate not only innovation, but governance discipline.
Whether ISO/IEC 42001 becomes a widely adopted certification benchmark or remains a guiding reference, it signals a broader shift: AI systems are now expected to meet the same standards of accountability, documentation, and continuous improvement as other critical business systems.
dicentra will continue to monitor developments in AI governance standards and share practical insights as this area evolves.