ISO/IEC 42001: A 'Management System'—The Concept of Translating AI Governance into 'Procedures'

0. Introduction

This is a follow-up to the following articles. Each can be read in one minute.

International standardization and certification currently have almost no public recognition. However, in the AI era, there will be an increasing number of areas where laws cannot keep up, and legal systems will diverge across countries. I believe this is a predetermined path. In such a situation, I am convinced that international standardization and certification will be the ones to step into the spotlight.

AI governance is, after all, a team sport. No matter how impressive the principles or ethical guidelines are, the typical points where accidents (legal, social, or technical failures) occur are in the gaps of the 'handoffs'—from strategy to implementation, implementation to operation, and operation to legal, risk, and security. When these gaps rupture, it manifests in the form organizations hate most: 'no one knows who decided what.'

ISO/IEC 42001 is a standard that requires these gaps to be stitched together as a 'management system' (Note 1). And what is important is that this is not a 'technical specification' that directly dictates model accuracy or architecture, but rather a framework (policies, processes, division of responsibilities, records) that drives organizational decision-making and operations (Note 1).

1. The Scope of the 'AI Management System' Concept

ISO/IEC 42001 covers not only organizations that develop AI but also those that provide AI and those that use AI (Note 1). In this respect, its stance of treating AI not as a laboratory artifact but as a component of business operations is clear.

AI is a component, but it is also a component whose behavior can change depending on the environment. Learning/updates, data fluctuations, changes in the operating environment, and differences in how users use it can cause different accidents even with the same model. Therefore, if you treat AI with the mindset of 'release it once and you're done,' legal, audit, and field operations will malfunction. A management system is a tool for institutionalizing this as 'continuous improvement' (Note 1).

2. Law Questions Not Only 'Results' but Also 'Processes'

Discussions about AI and the law are often drawn to the conclusion of 'who bears the responsibility.' However, in practice, there are many situations where an 'explainable process' is questioned before the issue of responsibility. What regulators, business partners, auditors, and courts want to see is not a theological debate, but a history of (i) how risks were identified in advance, (ii) how decisions were made, (iii) how they were recorded, and (iv) how they were corrected in the event of an accident.

In this sense, the 'management system' of ISO/IEC 42001 can be read as a device that translates legal risk from 'statutes' into 'procedures.' Of course, having procedures in place does not mean you are exempt from liability. However, if procedures do not exist (or exist only in form and are not actually followed), accountability collapses instantly. A management system at least creates a 'foundation for accountability.'

3. The Starting Point of Failure as Indicated by the 'Manual for CEOs'

What is interesting about ISO/IEC 42001 is that it is not a standard aimed only at engineers, but one that assumes a multi-track decision-making structure including management, CIOs/CTOs, CISO, CROs, and GCs (General Counsel) (Note 2). In practice, AI conflicts arise more often when it is unclear what risks to accept (risk appetite), who has the authority to stop it, and how to record the fact that it was stopped, rather than whether the model is good or bad.

In practical materials, it is often emphasized that AI governance failures occur at 'departmental boundaries,' and therefore cross-functional collaboration is necessary rather than siloed implementation (Note 2). This is painfully true. AI cannot be completed by legal alone, nor can it be completed by development alone. That is precisely why a 'common language' called a management system is needed.

4. The First Step Should Be an "AI Inventory"

Before governance, you cannot govern what you do not understand. The first necessity is an AI inventory. It seems important to list not only internally developed models but also SaaS, vendor-embedded solutions, external APIs, and even the "semi-AI" running behind internal tools.

The moment this inventory is completed, the quality of the discussion changes. It shifts from "Generative AI is scary" to concrete issues such as "In which business processes is it used, what inputs are used for what outputs, who is the final decision-maker, and what is the impact of errors?" Some practical materials also list the formation of cross-functional teams, AI inventory, initial risk assessment, and setting risk appetite as the foundational phase (Note 2). This order is not just an ideal; it is a survival strategy for the front lines.

5. Returning Risk Management from "Paperwork" to "Decision-Making"

If left unchecked, AI risk management becomes a "paper ritual." People are satisfied with filling out checklists, creating risk registers, and keeping minutes of committee meetings. However, what matters is "who decided what, what was rejected, and what was accepted with conditions."

The key to the AI risk register presented in practical materials is, ultimately, not just the identification, assessment, and response to risks, but the design of acceptance and escalation (Note 2). From a legal perspective, this is the watershed moment for whether you can "explain it later." AI defects cannot be reduced to zero (in fact, organizations that claim they can are dangerous). Since they cannot be reduced to zero, you must have a way to explain the criteria by which they were accepted.

6. Domestic and International Frameworks Are Converging on a "Risk-Based" Approach

Japan's "AI Business Guidelines (Version 1.0)" also emphasize a "risk-based approach," where the level of countermeasures is adjusted according to the magnitude of the risk, while acknowledging that excessive measures can hinder utilization (Note 4). The US NIST AI RMF also states that while the framework itself does not define risk appetite, it clearly notes that risk appetite can be influenced by legal and regulatory requirements and is context-dependent (Note 3).

The EU AI Act also places risk-based regulation at its core (Note 5). What is important here is not just "knowing the regulations," but the fact that the "model of accountability required by regulations" is shifting toward processes of risk management, recording, supervision, and improvement. ISO/IEC 42001 can function as a template for implementing this "model" within an organization.

7. Contracts and Procurement Are the "Front Lines" of Governance

When discussing AI governance, people tend to focus on policy documents and committee design. However, the actual front lines of practice are contracts and procurement. If you use external APIs or vendor products, the presence of model cards, the feasibility of logs and audits, reuse for training, data ownership, notification obligations during incidents, sub-processors, cross-border transfers, and notifications during updates will determine the success or failure of governance.

The idea of positioning contract templates and vendor management protocols as "components of a management system," as seen in practical materials, is legally sound (Note 2). However, contract clauses are not a panacea. Clauses that the operational side cannot uphold simply make the organization fragile. Contracts must be consistent with the granularity of actual operations.

8. It Ultimately Converges on Three Questions

Whether you introduce ISO/IEC 42001 or not, it seems that in practice, you cannot escape the following three questions.

First, who decides the risk appetite? This should not be decided by the "CRO," but rather in a way that management takes responsibility (Note 2) (Note 3).

Second, who has the authority to stop it? As AI spreads as "convenient automation," the authority to stop it becomes ambiguous. If an accident occurs while it remains ambiguous, everyone who failed to stop it could be held responsible.

Third, what should be kept as evidence? Logs, data, evaluation results, decision-making, exception handling, incident response, and recurrence prevention measures. If these are scattered in a way that they "cannot be reconstructed later," accountability will collapse.

ISO/IEC 42001 is not magic. However, it is a quite powerful tool for bringing AI governance back from "abstract theory" to "operations." The maturity of governance is measured not by the loftiness of its philosophy, but by the durability of its procedures. In short, it is about building a skeleton that will not break in the event of an accident. The reason legal departments get involved in this is not to brandish legal clauses, but to ensure the framework of accountability is built in a way that "leaves evidence."

The above content can be said to contribute to the transparency assurance discussed previously.

(Other References)

(Magazine) "Random Thoughts on AI and Law"

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