SAKURA LAW OFFICE | LEGAL UPDATE BY MANAGING PARTNER KENSHIRO MICHISHITA
September 24, 2026
SAKURA Law Office | Kenshiro Michishita, Managing Partner
SAKURA Law Office has published the eighth installment of the Legal Update series by Managing Partner Kenshiro Michishita: “AI Hallucinations and Corporate Legal Liability — Verification Frameworks and Governance for Customer Communications, Decision-Making and External Disclosures (2026).”
As the corporate use of generative AI expands beyond drafting and summarization into customer communications, sales materials, advertising, internal review, contract negotiations, recruitment, human resources, investment decisions, risk assessment and other consequential activities, the problem commonly described as “hallucination” — where an AI system produces inaccurate information in a highly plausible form — is no longer merely a technical quality issue. It has become a matter of legal liability, internal control and corporate reputation.
Generative AI is fluent and often persuasive in form. That very quality can make an inaccurate answer more dangerous: a user may fail to recognize the error, send it to a customer, incorporate it into a management paper, publish it on a website, use it in advertising, or treat it as a due diligence finding concerning a third party. Non-existent cases, fabricated statistics, outdated statutes, inaccurate corporate information and fictitious sources must all be understood as risks that may arise in the use of generative AI.
The correct response is not to reject generative AI merely because hallucinations occur. AIST’s Generative AI Quality Management Guidelines explain that hallucinations are linked to the underlying characteristics of generative models and that, even where mitigation measures are applied, eliminating them completely is difficult both in practice and in principle. Corporate governance should therefore not be designed on the assumption that an AI system will never be wrong. The relevant question is where an error may arise, what harm it could cause, and what verification and escalation mechanisms are required before the output is relied upon.
This Legal Update examines, as of September 24, 2026, the legal consequences of inaccurate AI outputs under Japanese law, including contractual liability, tort liability, allocation of responsibility between AI vendors and users, advertising regulation, personal data accuracy, defamation and reputational harm, Human-in-the-Loop controls in higher-risk use cases, the limitations of RAG and citation features, evidence preservation, incident response and board-level AI governance.
Executive Summary
Japanese law does not contain a single general rule requiring every AI output to be reviewed by a human before use. At the same time, where a company uses AI-generated content in customer communications, advertising, contractual performance, internal decision-making or communications to third parties, the legal assessment generally focuses on the acts and responsibilities of the company and the relevant human actors, not on the AI system as an independent legal person. Depending on the promised level of quality, foreseeability of harm, professional context, impact on third parties and the nature of the information involved, inaccurate AI output may engage liability for non-performance, tort, misleading representations, data protection violations or other legal consequences. Effective governance therefore requires more than a formal prohibition on AI. It requires risk-based verification standards, meaningful human involvement in important decisions, confirmation of primary sources where appropriate, preservation of logs and evidence, and an operational process for stopping, correcting and communicating inaccurate information when it reaches external audiences.
1. What Is an AI Hallucination? — Not Every Inaccurate Output Has the Same Cause
The term “hallucination” is generally used to describe a generative AI system producing information that is incorrect but presented in a plausible manner. For corporate governance purposes, however, it is not useful to treat every inaccurate output as though it arose from the same cause or creates the same legal risk.
A model may invent information that does not exist. It may provide an answer based on information that was current only as of its training cut-off. A RAG system may retrieve the wrong document. The system may retrieve the correct document but misread it. It may make a numerical or date calculation error. Or the user may provide an inaccurate premise and the system may reason from that premise correctly. These scenarios require different technical and legal responses.
The legal question is therefore not whether an error fits a particular technical definition of “hallucination.” The material questions are how the company obtained the information, how it was used, to whom it was communicated, what harm or rights infringement followed, and where the control process failed. Incident analysis should decompose the full chain — input, retrieval, model, prompt, external tools, human review and final use — rather than stopping with the statement that “the AI was wrong.”
2. Hallucinations Should Be Treated as a Quality Risk to Be Managed, Not an Exceptional Malfunction
AIST’s Generative AI Quality Management Guidelines, published in May 2025, explain that the causes of hallucinations in large language models may arise at multiple stages, including training data, model training and the generation process. The guidelines further recognize that, even where various countermeasures are applied, fully eliminating hallucinations is difficult in practice and in principle.
This has important consequences for legal and governance design. If a business process is built on the assumption of zero AI error, the control framework is detached from the technology’s actual characteristics. Companies should instead combine process design that prevents an error from immediately causing external harm, review procedures capable of detecting material mistakes, and correction mechanisms that can be activated quickly when inaccurate information has already been communicated.
Accordingly, AI quality should not be evaluated by average accuracy alone. The seriousness of potential error, frequency, detectability, reversibility, number of affected persons and the ability to contain or correct the impact should all be part of the risk assessment.
3. Japan’s AI Framework Emphasizes Human Judgment and Risk-Based Controls
Under the Japanese AI Act, the Government adopted the Guidelines for Ensuring the Appropriateness of Research and Development and Utilization of AI-Related Technologies on December 19, 2025. Those guidelines identify AI misjudgment and hallucinations as examples of technical risk and emphasize principles including human-centricity, safety, transparency and accountability. A core concept is that humans should retain ultimate judgment as to the scope and conditions under which AI is used.
The AI Guidelines for Business Ver1.2, issued on March 31, 2026, similarly encourage AI users to understand the accuracy and risk characteristics of AI outputs and to verify relevant risk factors before relying on them.
These frameworks do not mean that every output must be re-performed manually in the same way. The more appropriate approach is risk-based: the level of control should vary according to the use case, the persons affected, the significance of the decision and the magnitude of possible harm.
4. AI Is Not the Legal Liability Holder — “The AI Said It” Is Not a General Defence
As of September 24, 2026, Japanese law does not generally recognize a generative AI system as an independent legal person capable of bearing contractual or tort liability in the same manner as an individual or corporation. Where a company integrates generative AI into its operations and uses its outputs in customer service, advertising or decision-making, legal responsibility is generally assessed by reference to the acts and obligations of the company and the persons involved.
The fact that content was automatically generated, or that the company did not subjectively realize that the output was inaccurate, does not by itself eliminate legal exposure. Relevant questions include whether it was reasonable to use AI for that purpose, whether known limitations were understood, what verification would ordinarily be expected in the circumstances, and whether foreseeable risks were addressed by appropriate controls.
From a corporate-law perspective, AI should therefore be treated as a tool or component within the company’s performance and information-processing systems, not as a mechanism that creates a gap in accountability.
5. Incorrect Information Provided to Customers — Contractual Liability Depends on the Service Promised
If a company owes contractual obligations to provide information, advice, research, estimates, support or another service, and it uses inaccurate AI output in performing those obligations, liability for non-performance under the Japanese Civil Code may arise where the contractual standard is not met.
The mere fact that AI was used does not automatically create liability. The analysis depends on matters such as the content of the contractual obligation, whether accuracy formed part of the agreed service, whether disclaimers or limitations of liability apply, whether causation and loss can be established, and whether the company is legally responsible for the non-performance.
The context in which the output is presented also matters. A company that represents an AI-generated response as expert-reviewed or guaranteed information is in a different position from one that clearly identifies the response as automated reference information and directs users to a separate formal confirmation process for material matters. Contractual liability in AI-enabled services should be analyzed by asking what level of quality, verification and reliance the company actually promised to the customer.
6. Harm to Third Parties — Tort Liability and Employer Liability
Where inaccurate AI-generated information causes loss to a person with whom the company has no contractual relationship, tort liability under Article 709 of the Japanese Civil Code may become relevant. As a general matter, the analysis considers intent or negligence, infringement of a legally protected right or interest, loss and causation.
For example, publishing unverified false information about a third party may infringe that person’s reputation or business credit. Where an employee uses generative AI in the course of business and causes harm to a third party, employer liability under Article 715 of the Civil Code may also arise if the statutory requirements are satisfied.
Again, the legal conclusion does not follow simply from the fact that AI was used. The inquiry is whether reasonable verification measures were taken in light of the importance of the information, ordinary professional standards, known limitations of the AI system, the expertise of the user and the potential effect on third parties.
7. Liability of AI Service Providers — Contractual Allocation Matters
When inaccurate output originates from an external AI service, businesses sometimes assume that the AI vendor will bear responsibility. In practice, the allocation of risk depends heavily on the service description and contractual terms. Standard generative AI terms may disclaim output accuracy, require users to verify important results, and limit liability for certain categories of loss.
The position may differ where an AI system has been custom-developed for a specific business process and the contract includes performance metrics, acceptance criteria, testing procedures, warranty obligations, incident response requirements, data-update obligations, RAG configuration requirements or ongoing monitoring commitments. Failure to satisfy those contractual requirements may create vendor liability.
For that reason, responsibility should be addressed before deployment. Contracts should clarify what quality, if any, the provider warrants; who is responsible for validating outputs; whether higher-risk use cases are within scope; whether material model changes trigger revalidation; and how indemnity, damages and liability caps apply when inaccurate output causes harm.
8. Advertising, Websites and Sales Materials — Liability for Misleading Representations
Generative AI is increasingly used to draft advertising copy, product descriptions, comparison tables, FAQs and sales materials. If AI-generated content is inaccurate, however, the fact that the statement originated from AI does not remove the company from the scope of advertising regulation.
The Consumer Affairs Agency has made clear that misleading representations under the Act against Unjustifiable Premiums and Misleading Representations may be regulated even where the business did not intentionally make a false statement. Accordingly, if AI generates inaccurate claims regarding product performance, pricing, achievements, origin, rankings or comparative superiority and the company publishes those claims, the company may face regulatory risk as the party responsible for the representation.
Marketing workflows should therefore verify not only whether the wording is persuasive, but whether numerical claims, comparisons, rankings, awards, third-party evaluations and performance statements are supported by evidence. AI may automate drafting; it does not automate the company’s responsibility for substantiation.
9. False Information Affecting Reputation, Business Credit and Privacy
Generative AI may produce information about identifiable individuals or companies, including alleged criminal history, misconduct, professional background, financial condition or litigation history. If inaccurate information is disclosed externally, issues such as defamation, infringement of business credit and privacy may arise.
Particular caution is required in counterparty screening, M&A due diligence, recruitment background checks, anti-social forces screening and competitor research. AI can be useful for summarization and search assistance, but adverse information should be verified against reliable primary or official sources before it is relied upon for a material decision.
Even where the information remains internal, a separate risk arises if inaccurate AI-generated information affects recruitment, performance evaluation, transaction approval or termination of a business relationship. In such cases, accuracy obligations and correction rights under data-protection law may also become relevant.
10. Storing Incorrect Personal Information — Accuracy Obligations Under the APPI
Japan’s Personal Information Protection Commission has cautioned that outputs from generative AI services may contain inaccurate personal information. Article 22 of the Act on the Protection of Personal Information requires businesses, within the scope necessary to achieve the stated purpose of use, to endeavor to keep personal data accurate and up to date.
Accordingly, where AI-generated information about an individual is entered into a customer database, applicant tracking system, CRM, human-resources database or another personal-information database, the company should not assume the output is accurate merely because it appears specific and detailed. Continued use of incorrect personal data may raise issues under the APPI’s accuracy principle.
Where retained personal data is factually incorrect, an individual may in certain circumstances request correction, addition or deletion under Article 34 of the APPI. A distinction must nevertheless be drawn between inaccurate statements of fact and evaluative judgments based on facts; the legal treatment may differ depending on the nature of the information.
11. Higher-Risk Sectors Require Stronger Controls
The same AI error has very different consequences depending on its context. An inaccurate suggestion used for internal brainstorming is not equivalent to an error affecting financial products, medical matters, legal advice, recruitment, employment, credit decisions, safety management or another high-impact activity.
The Financial Services Agency’s AI Discussion Paper (Version 1.1), published in March 2026, organizes risk-reduction measures for customer-facing generative AI services around design and pre-deployment testing, appropriate explanations and cautions at the point of use, post-deployment validation and monitoring, and governance supporting the entire lifecycle. It also identifies practices such as maintaining human involvement and presenting supporting documents that users can verify as methods for reducing hallucination risk.
The underlying principle is not limited to financial institutions. Where inaccurate AI output could directly affect life, health, property, employment, significant contracts, reputation or legal rights, the output should generally not operate as the sole final decision. Review by a suitably qualified person with appropriate authority becomes increasingly important as the consequences increase.
12. RAG Reduces Some Risks, but It Does Not Eliminate Hallucinations
Enterprise AI systems frequently use retrieval-augmented generation, or RAG, to search internal policies, manuals, agreements, FAQs and other documents and then generate answers based on the retrieved materials. RAG can materially improve traceability and relevance, but it does not eliminate the risk of false or misleading output.
The Financial Services Agency has noted that even where RAG is used, inadequate system design may result in incomplete or inappropriate source selection and inaccurate output may remain. The underlying document may be outdated, access permissions may be wrong, the system may retrieve an irrelevant but similar document, or the model may misinterpret a correct source.
Companies using RAG should therefore manage the quality and approval status of source data, document currency, retirement of superseded materials, access controls, source citation, and the behavior of the system when a reliable answer cannot be produced.
13. A Citation Does Not Automatically Make an Answer Correct
Some generative AI systems display websites, document names, URLs or quoted passages together with their answers. Citation features are valuable because they increase verifiability, but the presence of a citation does not itself establish that the overall answer is correct.
An AI system may fabricate a source, cite a real document that does not support the stated conclusion, selectively extract language out of context, or rely on an outdated version of a document.
For important decisions, users should therefore verify that the cited source exists, that the original text actually says what the AI claims, that the source remains current, and that the conclusion reasonably follows from the source. For statutes, cases, administrative guidance, financial figures and other primary-source matters, the underlying official material should be checked wherever practicable.
14. Human-in-the-Loop Means More Than a Human Glancing at the Output
Human-in-the-Loop is frequently proposed as a solution to hallucination risk. Yet a human reviewer who merely glances at a long AI-generated document for a few seconds before approving it may provide little meaningful risk reduction.
Meaningful human involvement requires that the reviewer understand what must be checked, have access to the underlying evidence, possess authority to reject or modify the AI output, have sufficient time to review it, and operate under a clear allocation of responsibility.
In specialized areas such as law, tax, medicine or finance, assigning “final review” to a person who lacks the expertise needed to identify an error may amount to formal rather than substantive oversight. Human-in-the-Loop controls therefore require careful consideration of who is actually placed in the loop.
15. Verification Standards Should Be Tiered by Risk
It is generally impractical to verify every AI-generated statement against primary materials. Doing so would undermine much of the efficiency benefit of generative AI. Equally, leaving all verification to individual user discretion is not an adequate governance model. A tiered approach is therefore preferable.
For low-risk uses such as rephrasing already-public text, brainstorming or stylistic editing, ordinary business review may be sufficient. Medium-risk uses, such as customer emails, sales materials or internal review documents, may require verification of material facts and figures, source checks and management approval where appropriate. High-risk uses, including contract execution, financial decisions, recruitment and employment, legal advice, medical or safety-related matters and external public statements, may require primary-source verification, specialist review, dual approval or restrictions on automated sending.
AI control should therefore be designed as a question of verification intensity matched to the use case, rather than a binary choice between “permitted” and “prohibited.”
16. Logs and Evidence Preservation Are Critical After an AI Error
When inaccurate AI-generated information reaches an external audience and a dispute or loss arises, the ability to reconstruct how the output was produced becomes critically important. If the organization cannot identify the user input, AI output, source documents, model or service version, prompts, RAG results, human edits, approvals and final communication, both root-cause analysis and allocation of responsibility become significantly more difficult.
External AI services are updated continuously, and entering the same prompt at a later date may not reproduce the same answer. For material use cases, companies should consider retaining sufficient logs or snapshots to allow meaningful investigation after an incident.
At the same time, logs may contain personal data, confidential information and other sensitive content. Retention scope, access controls, retention periods and deletion must therefore be designed as part of the broader information-governance framework.
17. First Response to External Misinformation — Stop, Correct and Contain Before Debating Liability
When AI-generated misinformation reaches customers, websites, social media, advertising or business partners, the first priority is usually to stop the inaccurate information from continuing to be used or distributed and to establish the correct information. Assigning blame can follow once the immediate risk is contained.
In practice, the company should identify where the information was published or sent, suspend automated distribution or the relevant AI response, determine the accurate position, and make appropriate corrections or explanations to affected persons. Where the misinformation concerns a third party’s reputation, business credit or personal information, the need for deletion or correction may be particularly urgent. Misleading advertising may require suspension and remediation; contractual performance may require explanation or re-performance; inaccurate personal data may require correction procedures.
At the same time, the company should preserve relevant prompts, outputs, logs, source materials and approval history. It may also be necessary to contact the external AI provider to request preservation of service logs or other technical information. Relevant support channels and retention periods should therefore be understood before an incident occurs.
18. Remediation Requires More Than Improving the Prompt
After a hallucination incident, organizations sometimes attribute the problem solely to poor prompt design. Material incidents, however, often result from multiple factors across the model, source data, retrieval design, prompt, interface, access rights, human review and surrounding business process.
A proper remediation review should identify where the error originated, where it should reasonably have been detected, and why it was nevertheless allowed to reach final use. Controls may then include improving source data, changing refusal behavior, adding automated validation of critical fields, redesigning review screens, strengthening human approval, narrowing the permitted use case, or changing the model or vendor.
Where the same type of incident could occur across multiple departments, remediation should not end with a warning to the individual employee. Lessons should be incorporated into organization-wide AI policy, training and risk management.
19. Internal AI Policies Should Be More Specific Than “Do Not Blindly Trust AI”
A policy stating only that employees must “verify AI answers” does not tell staff what must be checked or to what standard. Effective rules should specify, for example, that externally used facts and figures must be supported by reliable evidence, statutes and cases should be verified against primary sources, adverse information about third parties should not be relied upon solely because it appears in AI output, high-risk uses require authorized approval, and certain categories of automated external sending are prohibited.
Systems should also be permitted to say that an answer is unknown or requires confirmation rather than being forced to generate a confident response. An organization’s culture should reward appropriate uncertainty in material matters rather than treating immediate answers as inherently superior.
Accuracy management is not solely the responsibility of engineering or IT. Legal, compliance, information systems, risk management and business functions should jointly define which categories of error are unacceptable and which can be corrected without material harm.
20. Boards Should Focus on Where Material AI Errors Can Escape Into the Real World
Boards and senior management do not need to understand every technical performance metric. They should, however, understand where AI is used in important business processes and where an inaccurate output could materially affect customers, financial performance, compliance, employment, corporate value or third-party rights.
A model with a high average accuracy rate may still create unacceptable risk if a single severe error each year could cause major damage. Conversely, a system that produces frequent low-consequence errors in internal brainstorming may present limited legal exposure.
Accordingly, AI governance should not rely solely on average model accuracy. Management reporting should also consider material incident scenarios, effectiveness of human oversight, monitoring, escalation, incident frequency, time to correction and the scale of external impact.
21. SAKURA Law Office Perspective: The Core of Hallucination Governance
The two most dangerous responses to AI hallucination are the extremes: “it is AI, so mistakes are unavoidable and therefore acceptable,” and “AI must never make a mistake.” The first removes necessary accountability; the second ignores the actual characteristics of the technology and may drive use outside formal governance.
The better approach is to accept that AI can be wrong and identify the points at which an error could become a material legal or business loss. Controls should then be placed at those points.
For customer-facing responses, this may mean pre-deployment validation and ongoing monitoring. For advertising, it means substantiation. For legal analysis, it means primary-source and lawyer review. For information about individuals, it means source confirmation and correction processes. For AI procurement, it means contractual allocation of responsibility. For important business processes, it means logs and meaningful Human-in-the-Loop controls.
The purpose of AI law and governance is not to teach organizations never to trust AI. It is to define, with precision, how far AI may be trusted and where accountable human verification must begin.
22. Illustrative Scenarios
First, consider a customer-support AI that gives a customer inaccurate information about cancellation rights and the customer suffers loss by relying on that answer. The legal analysis would consider the contractual service, the legal status of the AI response, disclaimers, resulting loss, and the company’s system for detecting and correcting incorrect information. Merely displaying a label stating “AI-generated response” would not automatically eliminate all responsibility.
Second, consider a marketing employee who uses generative AI to draft product copy and publishes a fictitious test result or award. Because the company is responsible for the representation, Japanese advertising regulation may apply even where the false statement originated from AI.
Third, consider a recruiter who asks generative AI about an applicant and receives a fabricated criminal-history allegation, which then influences a rejection decision. Issues may arise regarding reputation, privacy, data accuracy, fairness in decision-making and the reliability of the recruitment process. Material adverse information should not be accepted solely because an AI system generated it.
Fourth, consider a legal department that relies on an AI-generated interpretation of a contract or statute that cites a non-existent case or an outdated rule, and management makes a significant decision on that basis. The central governance failure may be less about the AI itself than about why the organization’s internal control framework allowed a consequential decision to proceed without primary-source verification.
23. Frequently Asked Questions
Q1. If ChatGPT or another AI system gives a wrong answer, is the AI provider always responsible?
No. Responsibility between the AI provider and the customer depends on the service, terms of use or negotiated agreement, warranties, liability limitations, intended use and the user’s own verification responsibilities. Where a company communicates AI output to its own customers, the company may also face contractual or tort liability in its relationship with those customers.
Q2. Must every AI-generated answer be reviewed by a human?
Japanese law does not impose one universal human-review rule for every use of AI. In practice, the appropriate standard depends on the use case, the potential consequences of error, whether the output will be communicated externally and the level of professional expertise required. Stronger verification is generally warranted where the output affects legal rights or external disclosures.
Q3. Is RAG sufficient to solve hallucination risk?
Not by itself. RAG is a powerful mitigation tool, but errors may still arise because source documents are outdated, retrieval is incorrect, or the model misinterprets the retrieved material. RAG should therefore be combined with source-data governance, update controls, citations and human verification where appropriate.
Q4. If the AI displays a source URL, can we rely on the answer?
For material matters, the organization should still confirm that the source exists, that the original material supports the proposition, that it remains current and that the AI’s conclusion follows from it. A citation is a verification aid, not a guarantee of correctness.
Q5. If inaccurate AI-generated advertising was not intentional, is there still a legal problem?
Potentially yes. Misleading representations under Japanese advertising law are not limited to deliberate falsification. Where a business uses an inaccurate AI-generated statement in marketing its own products or services, the representation may still fall within the scope of regulation and should be supported by evidence.
Q6. Is AI-generated information safe if it is used only internally?
Internal use often reduces external exposure, but it does not eliminate risk. If inaccurate information influences recruitment, employment, transaction approval, investment, contracting or another important decision, the organization may still suffer material harm. Internal use should therefore also be subject to risk-based verification.
Q7. What should a company do first when a hallucination incident occurs?
The immediate priorities are to stop further use or dissemination of the inaccurate information, correct the relevant content where necessary, and preserve prompts, AI outputs, source materials, logs and approval history. The company should then assess legal exposure under contract, data protection, advertising regulation, third-party rights and other applicable rules, and determine what notifications or remedial steps are required.
24. About the Kenshiro Michishita Legal Update Series
SAKURA Law Office publishes a continuing Legal Update series by Managing Partner Kenshiro Michishita on significant legal issues affecting corporate activity and the broader business environment. The series is designed not merely to summarize statutes or regulatory developments, but to identify where legal risk arises in real business processes and how boards, executives, legal teams, information-security functions and other practitioners should structure practical controls.
Previous installments have addressed enterprise generative AI governance, personal data protection, copyright, trade secrets and NDAs, AI service agreements, internal generative AI policies and AI agents. Future Legal Updates will continue to address AI, employment, M&A, cross-border transactions, Web3 and digital assets, crisis management and other areas of corporate law.
25. Matters on Which SAKURA Law Office Can Assist
SAKURA Law Office advises companies on legal and governance issues arising from hallucinations and inaccurate AI outputs, including risk assessment for customer-facing and internal AI services, Human-in-the-Loop design, internal AI policies, contractual terms and disclaimers for AI-enabled services, review of AI service agreements, use of AI-generated content in advertising and websites, incident response when inaccurate AI-generated information has been distributed, responses involving personal data or reputational harm to third parties, logging and evidence preservation, and the broader design of AI governance and internal controls.
Companies may seek advice before a system becomes customer-facing. We also assist at the PoC, internal-testing, RAG-design, vendor-selection and policy-development stages by examining the actual use case, source data, intended users, external impact and existing business processes and then identifying proportionate legal and operational controls.
Contact — AI and Generative AI Legal Services
For advice regarding AI hallucinations, customer-facing AI, internal AI policies, Human-in-the-Loop controls, AI service agreements, personal data, intellectual property, trade secrets, AI agents and other AI or technology-law matters, please contact SAKURA Law Office.
When contacting us, please indicate that your inquiry concerns “AI / Generative AI Legal Services” so that we can direct it promptly to the appropriate lawyer.
SAKURA Law Office
Kenshiro Michishita, Managing Partner
Ark Hills South Tower 4F, 1-4-5 Roppongi, Minato-ku, Tokyo 106-0032, Japan
Tel: +81-3-6910-0692
Written and supervised by Kenshiro Michishita, Managing Partner, SAKURA Law Office
Principal Sources
Financial Services Agency, AI Discussion Paper (Version 1.1)
Information-technology Promotion Agency, Japan (IPA), Information Security: 10 Major Threats 2026
Personal Information Protection Commission, Cautionary Notice on the Use of Generative AI Services
e-Gov Law Search, Civil Code of Japan
e-Gov Law Search, Act against Unjustifiable Premiums and Misleading Representations
This article provides general legal information based on laws, governmental publications and other public materials available as of September 24, 2026. It does not constitute legal advice or a legal conclusion with respect to any specific matter. AI service specifications, contractual terms, laws, regulations and governmental guidance may change. Specific matters should be assessed in light of the latest applicable law, contractual arrangements, technical environment, intended use and the relevant facts.