Hiroshima AI Reporting Framework

The Hiroshima AI Reporting Framework is open to organisations across the AI value chain, including developers, deployers, and providers of advanced AI systems. Participants are invited to report on their practices for managing risks and advancing trustworthy AI. Participation strengthens transparency, contributes to a shared understanding of organisational practices, and enables organisations to demonstrate their approaches to trustworthy AI.

Data Privacy and AI: G7 Hiroshima AI Process (HAIP) Transparency Report

Publication date: Oct 2, 2026, 07:34 AM GMT+2

Reporting period: Q3 2026

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Section 0 - Your role in the AI system lifecycle

Responding as (please select all that apply):

Deployer
Other (please specify): AI governance and oversight professionals, AI-Consultant, AI-Auditor, The following statements are based on our own experience with AI and also experience from customer projects.

If any of the fields above are selected, please indicate your organization size:

Micro enterprise: fewer than 10 employees

Section 1 - Risk Identification and Evaluation

1. How does your organization define and/or classify different types of risks related to AI?¹

Maintain an internal taxonomy, classification system, and/or set of risk area definitions
Align with specific external frameworks, standards, reports, and/or guidance documents such as taxonomies, classification systems, and/or sets of definitions for risk areas (e.g. the NIST AI Risk Management Framework, the ISO/IEC 42001, the OECD AI Principles, the EU General-Purpose AI Code of Practice, the International AI Safety Report). (please specify): ISO/IEC42001, OECD-Principles
Consult with external stakeholders (e.g., from academia, industry groups, civil society, or standard development organisations) when developing or updating risk taxonomies

How does your organization define thresholds at which severe risks, unless adequately mitigated, may be considered "unreasonable"? Please describe the framework(s) most relevant to your organization's role and context. If your organisation does not use formal thresholds, you may describe instead the policies, decision criteria, or governance processes you apply when assessing severe AI risks. You may also describe: • Specific examples of contexts in which your models or systems could, in your assessment, pose unreasonable or intolerable risk; • How mitigations are graduated by capability or propensity level; • How your organisation considers the specificities of different AI technologies, such as generative AI and agentic AI, including any specific risk assessment or governance mechanisms applied to agentic AI capabilities.

Thresholds will be defined based on ISO/IEC42001 Risk Assessment and ISO/IEC23894 AI Guidance on Risk Management, ISO/IEC42005 AI Impact Assessment

2. Both before and after deployment, what practices does your organization use to identify and evaluate vulnerabilities, incidents, emerging risks and misuse throughout the AI system lifecycle?

Impact assessments such as personal data protection assessment and fundamental rights impact assessments
Incident reports, including reports shared by other organizations

Please briefly describe your organization’s decision-making process for deploying an AI model or system including: who has decision authority; what evidence and criteria are required (e.g., performance, safety/security, robustness, fairness, privacy, compliance, independent external testing, evaluation, validation and verification (TEVV) or assurance); contextual caveats or limitations required in interpreting risk evaluation metric results; what approvals/sign-offs are needed; and what safeguards (e.g., mitigations, monitoring, or phased release) must be in place to move into deployment.

Definition of Approval-Process including mandatory information, e.g. Data Flow Diagram, Terms of use, Data Protection Agreement, Description of AI purpose and Data Categories, AI-Subjects (data subjects), Risk Classification based on EU-AI-Act, AI-Impact Assessment. To have information about performance, safety/security, robustness, fairness, privacy, compliance, independent external testing, evaluation, validation and verification (TEVV) or assurance would be valuable, but is currently not available. One receives hardly any information about this from the providers. Safeguards are actually monitoring measures. Definition of clear roles and responsible, e.g. AI-Owner, Approval is part of the management.

Following deployment, how do you use incident reports — including those shared by other organizations — to identify emerging issues, update risk assessments, and trigger corrective actions.

Currently underutilized, as such information is usually unavailable.

3. Does your organization have mechanisms to receive and/or share reports of risks, incidents or vulnerabilities by/with third parties?

Not applicable

4. If your organization has programs to incentivize risk(s) disclosure, incidents and systemic vulnerabilities, please share what type of programs you use?

Not applicable

5. Does your organization use or contribute to the development of international technical standards, best practices or tools for the identification, assessment, and evaluation of risks?

Contributes and uses

If contributes or uses, please select all that apply:

ISO/IEC standards (e.g., ISO/IEC 27001, ISO/IEC 42001, ISO/IEC 23894, ISO/IEC 42005)
NIST AI Risk Management Framework (AI RMF)
General Purpose AI Code of Practice

6. Does your organization collaborate with relevant stakeholders across sectors to assess and adopt risk mitigation measures to address risks that could have broad and potentially cascading socio-economic impacts? Please indicate which stakeholders you collaborate with, and whether collaboration is on a standing basis (Always-on), activated based on context (Ad hoc/as relevant), or there is no current collaboration.

	Always-on	Ad hoc / as relevant	No current collaboration
Relevant employees (security, legal/compliance, etc.)	✓
Public authorities / regulators / AI safety institutes / other public sector bodies and inter-governmental coordination structures		✓
Industry partners or industry fora	✓
Users (end users, enterprise customers, developers using APIs)		✓
Business customers / deploying organizations (if different from “users”)	✓
Supply-chain partners (cloud, data providers, etc.)			✓
Academia / researchers	✓
Independent auditors		✓
Standards bodies / multi-stakeholder initiatives (OECD.AI; ISO/IEC; IEEE; AIID etc.)	✓
Independent civil society organizations (e.g., for evaluations, audits, or policy review)			✓
Media / general public (people or communities indirectly impacted)			✓
Other(s) (please add any other relevant stakeholder groups):			✓
Not applicable

Please describe examples of how your organization collaborates. Examples may include: participating in multi-stakeholder or industry initiatives; partnering with academia, civil society, or the public sector; establishing internal or joint governance structures with external experts or in partnership with other efforts to share knowledge and jointly address risks.

HAIP-Partners Community

AI-Syndikat Germany - Platform for know-ledge sharing, use-case-assessments, exchange of experts (www.ki-syndikat.de)

exchange in ISACA Chapter Germany and DIN Media e.V.

Any further comments and implementation documentation.

Central risk register would be valuable and guidelines / reference values for metrics and thresholds, risk evaluation is mostly difficult, because since there is a lack of empirical data.

Section 2 - Risk Management and Information Security

7. Across the AI system lifecycle, what steps does your organization take to manage risks and vulnerabilities? Please indicate which specific practices, tools or metrics your organization uses at each lifecycle stage. You may refer to tools or metrics present in the OECD.AI Catalogue of Tools and Metrics or suggest additions. Respondents may complete only the lifecycle stages relevant to their organisation's role. Stages that do not apply may be left blank.

Plan and design

Collect and process data

Build, integrate and interpret model

Verify and validate

Deploy

Operate and monitor

Please provide a consolidated description of your organisation's approach to risk management across the AI system lifecycle, including how the practices identified above operate together and any cross-cutting governance, review or escalation processes. Please also indicate how often these practices are reviewed and what types of events trigger reviews and updates. Where relevant, please distinguish between different types of AI systems (e.g. generative or agentic AI).

Life-Cycle-point: Deploy, operate and monitor

triggers: incidents, changes of purpose, new data for fine tuning or new user group

general risk management life-cycle based on clause 6 and 8 ISO/EC42001 incl. Statement of Applicability and AI-Impact-Assessment ISO/IEC42005 (is currently being tested), Assessment of data privacy (Data Protection Impact Assessment), SWOT-Analysis, Integrated in ERM, CIA (ISMS) and BCM, periodical review is currently being tested (monthly, quarterly) for agentic AI

Please describe any measures used to ensure data quality and fairness across the data collection and curation stage (e.g., dataset audits, filtering training data to remove harmful content or personal information, fine-tuning on curated data, use of moderation tools, assessment of limitations to model generalisability).

Definition of Data Quality for Fine-Tuning and reasons for Fine-Tuning (Purpose)

Definition of Data Protection Measures for PII-Data (Anonymization)

8. What security measures are used in testing AI systems?

Not applicable

9. How does your organization protect intellectual property, including copyright-protected content?

9.A. Safeguards that promote lawful data use and transparency in AI training:

Consult and/or retain an intellectual property attorney to protect, enforce, and comply with, intellectual property rights.
Maintain a copyright policy that addresses data sourcing, content provenance, safeguards, and organizational roles and responsibilities

9.B. Safeguards that reduce the risk of dissemination of outputs that may infringe copyright:

Restricting outputs that raise ethical, human rights, or legal concerns—such as non-consensual intimate imagery, unauthorized deepfakes, or outputs that are substantially similar to copyrighted works.

10. How do you protect privacy and prevent your systems from disclosing confidential or sensitive data across the AI lifecycle?

Make information about data privacy, practices, and controls accessible to a diverse set of stakeholders
Provide organizational customers with controls to configure and implement privacy choices (e.g., data retention, logging, access, or processing settings)

Describe key privacy measures and data-protection standards applied.

ISO/IEC27001 incl. Annex A Controls, ISO/IEC27701 incl. Annex A Controls, Anonymization of PII-Data

11. How does your organization assess cybersecurity risks and implement policies to enhance the cybersecurity of advanced AI systems across the AI system lifecycle? Respondents may complete the checkbox subsections in 11.A–11.D or describe their overall approach under 11.E.

11.A. Governance, policy and assessments

Maintain or update security policies aligned with recognized standards (e.g., NIST CSF, ISO/IEC 27001, SOC 2, CIS Critical Security Controls), and review them regularly in response to emerging risks or regulatory guidance
Train staff on cybersecurity best practices and AI-specific threats, including how to recognize and respond to misuse or unintended outcomes of advanced AI systems
Integrate cybersecurity AI risks into broader cybersecurity risk management activities
Regularly review security measures and update them in response to emerging risks, incidents, or major releases

11.B. Technical and operational controls

Apply multi-factor authentication (MFA) and role-based access controls for advanced AI systems
Conduct secure AI development and/or deployment practices
These responsibilities are delegated contractually to model or infrastructure providers

11.C. Protection of sensitive or high value AI assets

Require NDAs or confidentiality agreements for staff and contractors with access
Track and log access to sensitive AI resources
Limit access to proprietary AI assets based on role or need-to-know
These responsibilities are delegated contractually to model or infrastructure providers

11.D. Insider risk protection detections

Provide staff training on cybersecurity best practices and AI-specific threats
Implement insider-risk escalation or response procedures
These responsibilities are delegated contractually to model or infrastructure providers

11.E. Please describe the main security measures your organization applies and indicate which are specific to particularly sensitive AI assets, where relevant.

ISO/IEC27001 incl. Annex A Controls, strict access and authorization concept

12. What is your organization's vulnerability management process? Does your organization take actions to address identified risks and vulnerabilities, including in collaboration with other stakeholders?

Internal vulnerability disclosure receipt and tracking processes
Reporting to regulators / authorities when relevant

13. What post-deployment practices does your organization use to monitor and respond to vulnerabilities, incidents, emerging risks and misuse of advanced AI systems?

Conduct post-deployment monitoring

14. Do you develop or deploy agentic AI systems (systems that can autonomously plan/act or use tools)?⁴

Yes

14.A. If yes, please indicate which categories of controls your organization applies. Please select all.

Human oversight or interruptibility mechanisms (approval requirements for sensitive actions, escalation paths, interruption interfaces)

Please provide any further comments and implementation documentation.

No answer provided

Section 3 - Transparency Reporting on Advanced AI Systems

15. Does your organization publish public reports and/or technical documentation related to the capabilities, limitations, and domains of appropriate and inappropriate use of advanced AI systems? Are these clear and understandable by non-technical audiences?

Not applicable

16. How does your organization share information with a diverse set of stakeholders (other organizations, governments, civil society and academia, etc.) regarding the outcome of evaluations of risks and impacts related to an advanced AI system?

Not applicable

Describe how evaluation results are shared and with which stakeholders (e.g. other organizations, governments, civil society and academia, etc). You may also indicate whether non-technical or plain-language summaries are provided for broader audiences.

currently not applicable

17. Does your organization publish public privacy policies addressing the use of personal data, user prompts, and/or the outputs of advanced AI systems?

18. How does your organization provide information about the sources of data used for the training of advanced AI systems, as appropriate, including information related to the sourcing of data annotation and enrichment?

Not applicable

Please provide links to any publicly available information.

not applicable

19. Does your organization demonstrate transparency related to advanced AI systems through any other methods?

Yes

If yes, describe additional method(s), including where relevant transparency measures related to AI agents

internal AI-Agent-Register incl. AI-Agent-ID and Description, Labeling as an AI agent, Data Protection Notice, Declaration of Consent, if necessary

20. Does your organization publish statistical insights derived from anonymised, aggregated usage data to advance understanding of AI adoption and its economic impacts in areas including occupations and tasks by region, sector and country? To this end, do you contribute to international cooperation efforts?

Not applicable

Please estimate the total staff time and annual expenses associated with conducting compliance activities and include any further comments and implementation documentation (optional).

1 FTE

Section 4 - Organizational Governance, Incident Management, and Transparency

21. How has AI risk management been embedded in your organization's governance framework?

Executive oversight

Please describe how AI risk management is integrated into governance structures, including: which roles or bodies have advisory versus decision-making authority; which roles or bodies have final accountability for AI risk-related decisions; and how often policies are reviewed or updated (e.g., following major incidents, regulatory changes, or material updates to systems).

Risk Reports and Statement of Applicability of Annex A ISO/IEC42001, decision-making authority is management (final accountability), Policy review in minimum once per year or after incidents, regulatory changes or material updates (just in time or in aggregated form, risk-based approach)

22. Are relevant staff trained on your organization's governance policies and risk management practices?

Yes

If yes, describe training methods (e.g., onboarding modules, regular sessions, and confidential channels to report concerns).

general AI-Competence Trainings - online trainings, selfstudy-trainings

AI and PII & GDPR - online trainings, selfstudy-trainings

AI-Impact Assessment - online trainings, selfstudy-trainings

reporting channels are implemented

23. Are steps taken to address reported incidents documented and maintained internally?

Yes

If yes, describe how incident records are kept and used for follow-up (e.g., incident logs, root-cause analysis reports, internal tracking systems). You may indicate whether and how you define escalation thresholds for when incidents must be surfaced to senior leadership or external parties.

Integration in regular jour-fixe-calls, reporting to management incl. recommendations to to prevent future incidents

24. Does your organization share research and best practices on addressing or managing risk?

Yes

If yes, describe how research or good practices are shared (e.g., publications, conferences, collaborations with other sectors).

Shared in publications, conferences and speeches, e.g. Data Science and AI Summit 2026 London, training courses, Publications https://www.dataprivacyandai.com/en-gb/neue-seite-267ed69ec

25. Does your organization use international technical standards or best practices for AI risk management and governance policies?

Yes

If yes, please indicate which standards or frameworks apply:

OECD AI Principles
NIST AI Risk Management Framework (AI RMF)
ISO/IEC standards (e.g., ISO/IEC 27001, ISO/IEC 42001)

If yes, describe which international technical standards or best practices your organization uses.

Orientation on the new AI-Standards of ISO/IEC, VDE SPEC 90012

Any further comments and implementation documentation.

No answer provided

Section 5 - Content Authentication & Provenance Mechanisms

26. What mechanisms, if any, does your organization put in place to allow end-users, where possible and appropriate, to know when they are interacting with an advanced AI system developed by your organization?

User disclosure (e.g., text notice, verbal statement, or system label)
Persistent visual or audio indicators (e.g., watermark, icon, chime, banner) during AI interaction
Distinct branding or labeling for AI-generated content (e.g., “AI-generated” tag, metadata label)
Disclosure to end-users when an AI system is acting autonomously or executing actions on their behalf (e.g., making purchases, sending communications, modifying files)

Provide any additional details or context on how or when users are informed they are interacting with advanced AI systems (e.g. if such mechanisms are planned, in testing, or applied only to certain products).

No answer provided

27. Does your organization use content provenance detection, labeling or watermarking mechanisms that enable users to identify content generated by advanced AI systems?

Not applicable

Any further comments and implementation documentation.

No answer provided

Section 6 - Research & Investment to Advance AI Safety & Mitigate Societal Risks

28. Does your organization advance research and investment in the following areas?

Other (please specify): not applicable at the moment

If yes, please describe key research or investment initiatives in the areas selected above (e.g., dedicated safety teams, funding of research programs, contributions to open-source safety tools, publishing research, partnerships and collaborative research initiatives). You may also indicate whether related outputs are publicly shared, shared with partners or within consortia or not publicly disclosed.

not applicable at the moment

29. Does your organization pursue research or investment related to socio-economic or environmental considerations in the development and deployment of AI?

Not applicable

Any further comments and implementation documentation.

No answer provided

Section 7 - Advancing Human and Global Interests

30. Does your organization support any digital literacy, education or training initiatives to improve user awareness and/or help people understand the nature, capabilities and limitations of advanced AI systems?

Yes

If yes, describe any digital literacy or training initiatives your organization supports (e.g., user education, public AI-education programs, school or university partnerships, community workshops). You may also indicate whether these efforts include or prioritise underserved or underrepresented communities.

No answer provided

31. Does your organization pursue AI projects that aim to deliver societal or public benefits, including through partnerships with civil society and community groups?

Yes

If yes, please provide examples, including any collaborations with civil society, community groups, or efforts to address broader societal challenges.

No answer provided

Any further comments and implementation documentation.

No answer provided

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