Prepare evidence for an AI governance review in Japan
Bring the data flow, task tests, approval rules, incident procedure and exit plan.
The buyer needs enough evidence to decide whether an AI application is suitable for a particular job. Show what information it can access, how outputs are tested, which actions need approval and who responds to failures. This guide explains how to prepare that evidence for an evaluation by the Japanese prospect’s business, IT and security teams.
Describe the task and the applicable requirements
State the users, intended decisions and uses that are outside scope. Ask which laws, sector rules, contracts and internal policies the buyer applies. METI and MIC published AI Guidelines for Business Ver1.2 on 31 March 2026; identify the version used in the review. A reference to a guideline is not a product certification or evidence that a specific control works.
Show the data flow and access rules
Include prompts, source documents, retrieval, model calls, tools, outputs, logs, backups and support access. Identify the parties and locations involved. APPI assessment depends on the actual handling and any applicable foreign-provision conditions, not inference location alone. Give the buyer the service terms and configured controls needed to assess that arrangement.
Provide test results and the remaining failures
Show representative Japanese tasks, scoring criteria, test dates and the model and application versions. Include unsupported questions, access restrictions and harmful or misleading outputs. Report where the system fails and how it responds. A benchmark or a demonstration is not a guarantee of performance on the buyer’s records.
Assign approvals and incident response
Decide which actions require a person, who can approve them and what information that person sees. Do not present a human checkpoint for every output as a universal legal rule. For incidents, document detection, containment, notification responsibilities and recovery. A tabletop test using an example such as disclosure of the wrong customer’s record can reveal gaps before launch.
Test changes and exit
Identify which model, provider, prompt or tool changes trigger a new evaluation. Define rollback and record who approves release. Test an export of the buyer’s data and configuration where the service supports it; document exclusions and time or cost conditions. Use an evidence list with owners and unresolved items so the purchasing decision does not rely on assurances given only in a meeting.
Sources and further reading
// Key Takeaways
What to remember
- Prepare evidence for the proposed application and its actual data flow.
- Report tested limits and assign the operating responsibilities.
- Demonstrate change, incident and exit procedures where applicable.
// FAQ
Frequently asked questions
Does domestic inference establish that an AI application meets all privacy requirements?
No. Review the complete data flow, service terms, parties, access and applicable transfer conditions. Domestic inference is one configuration detail.
Which documents help a buyer assess a proposed AI application?
A task and scope statement, data-flow diagram, test results and limits, approval rules, incident responsibilities and change or exit procedures, matched to the buyer’s requirements.
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