{
  "library_identifier": "ai_governance_global",
  "version": "R4",
  "date": "2026-07-27",
  "scope_note": "Authority MAP for AI governance regulatory and framework requirements across global jurisdictions. Triggered when an AM audit target makes AI governance, approval-first, human-in-the-loop, compliance-grade AI, automated decision, or AI content integrity claims. This config is the stable regulatory architecture matrix: which authority governs which AI governance domain, in which jurisdiction, at which canonical primary-source URL. It is NOT live facts. The AM audit session reads this config, fetches cited primary sources at session time, stamps as-of dates, and seals retrieved facts in a signed manifest. Source URLs are transcribed from operator-verified search results and primary authority pages; none are inferred from training data. Core structural finding this config operationalises: no active global regulatory framework accepts AI-only validation of AI output as compliance. All require human oversight that is genuine, documented, independently verifiable, and attributable to a named natural person with authority to intervene. This is not a gap or ambiguity \u2014 it is structural convergence across jurisdictions with no coordination mandate.",
  "claim_trigger_keywords": [
    "Approval-First AI",
    "AI governance",
    "AI compliance",
    "compliance-grade AI",
    "human-in-the-loop",
    "AI oversight",
    "automated decision",
    "AI-powered compliance",
    "AI audit trail",
    "governed AI",
    "responsible AI",
    "trustworthy AI",
    "AI transparency",
    "AI-reviewed",
    "AI content governance",
    "AI safety",
    "no automated decision-making",
    "source-bound answers",
    "human approval required"
  ],
  "am_sections_triggered": [
    "A",
    "D"
  ],
  "am_checks_triggered": [
    "A1",
    "A4",
    "D1",
    "D2",
    "D4"
  ],
  "tier_definitions": {
    "tier_1_structured_feed": "Machine-readable, automatable feed (download/parse). Worker fetches directly.",
    "tier_2_primary_html": "Primary authority, HTML/PDF only. Worker web_fetches the named page; provenance = URL + retrieval date. Scope clarification per Operator Ruling 1 (2026-07-25, OPERATOR_RULING_MEMO_AI_GOVERNANCE_SCOPINGS_R1.md): this two-field definition governs runtime audit serving only; build-time library entry composition is governed by the five-field custody standard (url, http_status, UTC retrieval timestamp, sha256 of retrieved bytes, byte size). Neither standard weakens the other.",
    "tier_3_trend_only": "Industry/research/news source. Signal and citation support ONLY. Never the citation of record for a binding rule."
  },
  "track_definitions": {
    "ai_content_governance": "Applies when target makes AI content claims \u2014 approval-first, source-bound, no hallucination, governed output.",
    "high_risk_ai_systems": "Applies when target's AI system meets high-risk classification criteria under applicable regime (credit, employment, healthcare, critical infrastructure, financial advice).",
    "all_ai_systems": "Universal requirement \u2014 applies regardless of risk tier or content type. Non-negotiable baseline."
  },
  "core_structural_finding": {
    "proposition": "AI oversight of AI output is structurally insufficient for regulatory compliance across all active global frameworks.",
    "basis": "Five independent regulatory bodies \u2014 EU, US (FINRA/NIST), UK (FCA/PRA), Singapore (MAS/IMDA), and multilateral (OECD/G7) \u2014 independently converge on identical structural requirement: human oversight must be genuine, documented, and attributable to a natural person with authority to intervene. Convergence without coordination mandate is the strongest available factual signal.",
    "implication_for_am_audit": "A public-facing AI governance claim that cannot be traced to a documented human-in-the-loop framework with tamper-evident audit records is a substantiation gap, not merely a disclosure gap. Resolution is structural, not editorial."
  },
  "categories": [
    {
      "category_id": "category_1_eu_mandatory",
      "category_title": "EU \u2014 MANDATORY (AI ACT)",
      "category_target": "Binding EU regulation governing all AI systems offered to or deployed by entities serving EU persons. Extraterritorial. Non-compliance penalties up to 7% of global annual turnover. Full high-risk system obligations enforceable 2 August 2026.",
      "authorities": [
        {
          "authority": "European Commission \u2014 EU AI Act (Regulation 2024/1689)",
          "jurisdiction": "EU-extraterritorial",
          "domain": "Article 14 \u2014 Human oversight: natural persons must monitor AI behavior, detect automation bias, interpret outputs, and retain authority to reject, override, or interrupt. Oversight personnel must be named, competent, and documented before deployment.",
          "track": "high_risk_ai_systems",
          "enforcement_date": "2026-08-02",
          "source_url": "https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai",
          "tier": "tier_2_primary_html",
          "note": "Article 14 is the definitive primary-source refutation of AI-only oversight. Natural persons required. Fetch this page at session time and log retrieval date."
        },
        {
          "authority": "European Commission \u2014 EU AI Act Article 12",
          "jurisdiction": "EU-extraterritorial",
          "domain": "Logging: AI systems must generate tamper-evident audit trails of relevant events as a built-in technical feature. Logs must capture inputs, outputs, and decisions in sufficient detail for traceability. Deployers must retain logs for minimum 6 months.",
          "track": "high_risk_ai_systems",
          "enforcement_date": "2026-08-02",
          "source_url": "https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai",
          "tier": "tier_2_primary_html",
          "note": "Tamper-evident is a design requirement, not a documentation requirement. A static log modifiable by an admin does not satisfy Article 12."
        },
        {
          "authority": "European Commission \u2014 EU AI Act Article 50",
          "jurisdiction": "EU-extraterritorial",
          "domain": "Transparency: AI-generated content must be labelled. Transparency obligations for AI systems active August 2026 per EU Commission July 2026 update. Code of Practice on marking and labelling AI-generated content published June 2026.",
          "track": "ai_content_governance",
          "enforcement_date": "2026-08-01",
          "source_url": "https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai",
          "tier": "tier_2_primary_html",
          "note": "Directly applicable to any company claiming AI-generated content is governance-compliant without disclosure of AI origin."
        },
        {
          "authority": "European Commission \u2014 EU AI Act GPAI Obligations",
          "jurisdiction": "EU-extraterritorial",
          "domain": "General-Purpose AI model providers face transparency and documentation obligations. Active August 2025. Applies to any company whose product is built on or integrates a GPAI model.",
          "track": "all_ai_systems",
          "enforcement_date": "2025-08-02",
          "source_url": "https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai",
          "tier": "tier_2_primary_html"
        }
      ]
    },
    {
      "category_id": "category_2_us_federal_ai",
      "category_title": "US FEDERAL \u2014 AI GOVERNANCE",
      "category_target": "US federal frameworks governing AI risk management, content supervision, explainability, and human oversight. NIST is voluntary but referenced by state laws and increasingly treated as the US baseline. FINRA requirements are binding for broker-dealers; technology-neutral application means the same supervisory standard applies to AI-generated content as human-generated content.",
      "authorities": [
        {
          "authority": "NIST \u2014 AI Risk Management Framework 1.1 (NIST AI RMF 1.1)",
          "jurisdiction": "US-federal-voluntary",
          "domain": "Four functions: GOVERN, MAP, MEASURE, MANAGE. GOVERN assumes accountable humans making decisions. MANAGE requires ongoing monitoring and human intervention capability. Generative AI Profile (NIST-AI-600-1, July 2024) adds 12 generative-AI-specific risk categories. February 2026 NIST initiative adds standards for autonomous AI agents: agent identity, action logging, and containment boundaries.",
          "track": "all_ai_systems",
          "source_url": "https://www.nist.gov/system/files/documents/2024/07/26/NIST.AI.600-1.pdf",
          "tier": "tier_2_primary_html",
          "note": "Referenced by Colorado, Texas, and other US state AI laws. Practical compliance baseline even without mandatory status. Fetch the AI-600-1 Generative AI Profile for the most current version."
        },
        {
          "authority": "FINRA \u2014 2026 Annual Regulatory Oversight Report (AI Section)",
          "jurisdiction": "US-federal-binding-broker-dealers",
          "domain": "AI outputs face the same supervisory standards as human-generated content. Firms must document how AI is used, test and monitor outputs, assign human accountability, and retain records related to AI-assisted decisions. Technology-neutral: supervision must focus on outcomes, not intent. Three novel risks flagged: autonomy (agents acting without human validation), scope creep, and auditability.",
          "track": "ai_content_governance",
          "source_url": "https://www.finra.org/rules-guidance/guidance/annual-regulatory-oversight-report",
          "tier": "tier_2_primary_html",
          "note": "Directly applicable to any company in or adjacent to financial services making AI governance claims about content production. FINRA Rule 3110 supervision requirements apply regardless of whether a human or AI produces the output."
        },
        {
          "authority": "FTC \u2014 Policy Statement on AI and Section 5 (March 2026)",
          "jurisdiction": "US-federal",
          "domain": "AI-generated content subject to unfair or deceptive practices standards. Unsubstantiated AI governance claims are a deceptive practice trigger. Section 5 applies to any AI claim that cannot be substantiated.",
          "track": "ai_content_governance",
          "source_url": "https://www.ftc.gov",
          "tier": "tier_2_primary_html",
          "note": "Fetch FTC AI guidance page at session time. Policy statement dated March 2026 per regulatory tracking sources."
        },
        {
          "authority": "CFPB \u2014 AI explainability for credit decisions",
          "jurisdiction": "US-federal",
          "domain": "Requires explainability for AI-driven credit decisions under existing obligations. Applicable when AI system influences access to credit or financial services.",
          "track": "high_risk_ai_systems",
          "source_url": "https://www.consumerfinance.gov/compliance/supervisory-guidance/",
          "tier": "tier_2_primary_html"
        }
      ]
    },
    {
      "category_id": "category_3_uk_ai_governance",
      "category_title": "UK \u2014 AI GOVERNANCE (FCA / PRA)",
      "category_target": "UK financial services AI governance requirements. FCA is shifting from traditional rule-making to continuous supervision and stewardship for AI. PRA model risk framework SS1/23 extends to AI-informed decisions.",
      "authorities": [
        {
          "authority": "UK Financial Conduct Authority (FCA)",
          "jurisdiction": "UK",
          "domain": "Continuous supervision model for AI in financial services. More than 80% of financial services firms already using or adopting AI \u2014 FCA policy focus has shifted from adoption to large-scale deployment governance. Firms must preserve trust, competition, and resilience. AI governance claims in financial services require substantiation.",
          "track": "high_risk_ai_systems",
          "source_url": "https://www.fca.org.uk/innovation/artificial-intelligence",
          "tier": "tier_2_primary_html",
          "note": "FCA speech at techUK Agents of Change event (2026) confirmed FCA is using sandboxes and supervision rather than static rules. Fetch the FCA AI innovation page for current guidance."
        },
        {
          "authority": "UK Prudential Regulation Authority (PRA) \u2014 SS1/23 Model Risk Management",
          "jurisdiction": "UK",
          "domain": "Model risk framework extends to AI-informed decisions. Human oversight for high-impact AI-informed decisions required alongside MAS, OCC, Bank of Thailand convergence.",
          "track": "high_risk_ai_systems",
          "source_url": "https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks",
          "tier": "tier_2_primary_html"
        }
      ]
    },
    {
      "category_id": "category_4_apac_ai_governance",
      "category_title": "ASIA-PACIFIC \u2014 AI GOVERNANCE",
      "category_target": "Singapore, Hong Kong, Australia, and regional Asia-Pacific AI governance frameworks. Singapore IMDA Agentic AI Framework (January 2026) is the first framework written specifically for autonomous agents. MAS guidelines are increasingly adopted as global benchmark. Bank of Thailand 2025 policy is explicit on human oversight for strategic AI functions.",
      "authorities": [
        {
          "authority": "Singapore IMDA \u2014 Agentic AI Framework (January 2026)",
          "jurisdiction": "SG",
          "domain": "First governance framework written specifically for autonomous AI agents, launched at Davos January 22, 2026. Graduated autonomy model: oversight intensity proportional to action impact. Every prior framework (NIST, ISO 42001, EU AI Act) was built for AI systems a person operates \u2014 IMDA framework addresses the gap where agents remove the human from the decision loop.",
          "track": "all_ai_systems",
          "source_url": "https://www.imda.gov.sg/resources/blog/blog-posts/2026/01/sgd-framework-agentic-ai",
          "tier": "tier_2_primary_html",
          "note": "Increasingly adopted as global benchmark for agentic AI governance. Fetch IMDA blog/publication page at session time to confirm current URL."
        },
        {
          "authority": "Monetary Authority of Singapore (MAS) \u2014 AI in Financial Services Guidelines",
          "jurisdiction": "SG",
          "domain": "Human oversight required for high-impact AI-informed decisions. MAS guidelines converge with PRA, OCC, and Bank of Thailand on the same structural requirement: institution remains responsible for decisions regardless of whether a human or AI agent made them.",
          "track": "high_risk_ai_systems",
          "source_url": "https://www.mas.gov.sg/regulation/explainers/veritas",
          "tier": "tier_2_primary_html"
        },
        {
          "authority": "Bank of Thailand \u2014 AI Risk-Management Policy (2025)",
          "jurisdiction": "TH",
          "domain": "Explicit: human participation or human oversight required whenever AI is used for strategic functions, defined to include credit approval, account opening approval, and approval of deposits, withdrawals, or transfers. Lifecycle-wide controls across data quality, model evaluation, explainability, and AI-specific cyber defenses.",
          "track": "high_risk_ai_systems",
          "source_url": "https://www.bot.or.th/en/financial-innovation/ai.html",
          "tier": "tier_2_primary_html",
          "note": "Cited in academic literature (arXiv 2606.22484) as one of four converging regulators requiring human oversight for high-impact AI decisions alongside MAS, PRA, and OCC."
        },
        {
          "authority": "Securities and Futures Commission (SFC), Hong Kong",
          "jurisdiction": "HK",
          "domain": "SFC circular dated 12 November 2024 to licensed corporations, titled 'Use of Generative AI Language Models': sets out SFC expectations on licensed corporations that offer services or functionality provided by AI language models in their regulated activities. Expectations address hallucination risk (AI language models providing plausible but factually incorrect responses) and performance drift (performance may degrade over time). Applies to AI language model-based third party products used in regulated activities. Supervisory circular; not binding statute.",
          "track": "ai_content_governance",
          "source_url": "https://apps.sfc.hk/edistributionWeb/gateway/EN/circular/openFile?refNo=24EC55",
          "tier": "tier_2_primary_html",
          "note": "Capture anchor: generative_language_modelspdf.pdf (sha256 e45ff626, 191813 B, 8 pages), admitted 2026-07-25 per Operator Ruling 2 (SFC e-distribution gateway js_gated_spa failure covers all gateway documents). source_url is the SFC's own document link for SFO/IS/036/2024 (refNo 24EC55), read from the circular's page on apps.sfc.hk by the Advisor 2026-07-27 and verified against the capture's content verbatim. The recorded pipeline failure URL (refNo 26EC32, the June-2026 circular) remains in pipeline_failure_reference as the failure that admitted this capture under Operator Ruling 2; it is not this instrument's URL. SFC June 2026 cybersecurity circular (SFO/IS/020/2026, sha256 6f2484d1, 344605 B) is on disk as supplementary per Operator Ruling 5 (2026-07-27).",
          "enforcement_date": "2024-11-12"
        },
        {
          "authority": "Hong Kong Monetary Authority (HKMA)",
          "jurisdiction": "HK",
          "domain": "HKMA circular dated 19 August 2024 to all Authorized Institutions, titled 'Consumer Protection in respect of Use of Generative Artificial Intelligence': board and senior management of authorized institutions remain accountable for all GenAI-driven decisions and processes; authorized institutions should adopt a human-in-the-loop approach during early GenAI deployment; addresses hallucination risk (generating outputs that seem realistic but are factually incorrect). Extends 2019 BDAI guiding principles on governance and accountability, fairness, transparency and disclosure, and data privacy and protection to generative AI use. Supervisory circular; not binding statute.",
          "track": "ai_content_governance",
          "source_url": "https://www.hkma.gov.hk/media/eng/doc/key-information/guidelines-and-circular/2024/20240819e1.pdf",
          "tier": "tier_2_primary_html",
          "note": "Covering circular anchor: 20241107-1-EN.pdf (sha256 1e113b6c, 220070 B), admitted per Operator acceptance of retrieval run 2026-07-26. Filename 20241107-1-EN is the BRDR docId, not an instrument date; instrument date 19 August 2024 confirmed inside file per R4 \u00a75.1 flag. Annex 1 on disk: HKMA_guidings.pdf (sha256 32b808cb, 154059 B), admitted 2026-07-25; carries Annex 1 (pp 5-7) from the 2019 BDAI guiding principles circular dated 5 November 2019 \u2014 Annex 1 content only is citable from 32b808cb. Annex 2 not on disk: honestly final absence."
        },
        {
          "authority": "Japan \u2014 Act No. 53 of 2025 (Act on Promotion of Research and Development, and Utilization of Artificial Intelligence-related Technology)",
          "jurisdiction": "JP",
          "domain": "Act No. 53 of June 4, 2025 on Promotion of Research and Development, and Utilization of Artificial Intelligence-related Technology: binding statute establishing Japan's basic AI governance framework; declares that artificial intelligence-related technology constitutes a fundamental technology for the development of Japan's economy and society; establishes basic principles including transparency; establishes AI Strategic Headquarters. Bilingual official English translation.",
          "track": "all_ai_systems",
          "source_url": "https://www.japaneselawtranslation.go.jp/en/laws/view/5066",
          "tier": "tier_2_primary_html",
          "note": "Anchor: r07Aa000530105je18.0.pdf (sha256 25a18618, 294573 B), admitted per Operator acceptance of retrieval run 2026-07-26; pipeline failure reference: record [1] (aigov_jp, http_403) in ai_governance_retrieval_absences_20260725.json. No enforcement_date stated in file: field omitted. Supporting file on disk: aiplan_2601_draft_en.pdf (sha256 0dba81ac, 465935 B) is a Phase II DRAFT provisional translation as of 19 June 2026 \u2014 not cited as a decided Plan; the decided Plan (23 December 2025) is referenced inside the draft. The AI Plan draft does not warrant a separate entry at this stage."
        },
        {
          "authority": "Republic of Korea \u2014 Framework Act on the Development of Artificial Intelligence and the Creation of a Foundation for Trust (Act No. 20676)",
          "jurisdiction": "KR",
          "domain": "Framework Act on the Development of Artificial Intelligence and the Creation of a Foundation for Trust (Act No. 20676, enacted 21 January 2025, enforcement date 22 January 2026): binding statute establishing Korea's basic AI governance framework; defines high-impact artificial intelligence including systems that perform judgments or evaluations with significant impact on the rights and obligations of individuals, such as hiring and loan screening. Enforcement date 22 January 2026.",
          "track": "high_risk_ai_systems",
          "source_url": "https://www.law.go.kr/\uc601\ubb38\ubc95\ub839/\uc778\uacf5\uc9c0\ub2a5\ubc1c\uc804\uacfc\uc2e0\ub8b0\uae30\ubc18\uc870\uc131\ub4f1\uc5d0\uad00\ud55c\uae30\ubcf8\ubc95/(20676,20250121)",
          "tier": "tier_2_primary_html",
          "enforcement_date": "2026-01-22",
          "note": "Anchor: FRAMEWORK ACT ON THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE AND THE CREATION OF A FOUNDATION FOR TRUST.pdf (sha256 4f8aa787, 119746 B), admitted per Operator acceptance of retrieval run 2026-07-26; pipeline failure reference: retrieval log aigov_kr records (http 200 but both HTML files are frame shells \u2014 fetch evidence only, not citable as the act). source_url is the law.go.kr URL where the official English translation is served, confirmed as the URL attempted by the retrieval pipeline. Enforcement decree not on disk: honestly final absence per ai_governance_retrieval_absences_20260725.json record [3] (http_404 on enforcement decree URL)."
        },
        {
          "authority": "Cyberspace Administration of China \u2014 Interim Measures for the Management of Generative Artificial Intelligence Services (\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd\u670d\u52a1\u7ba1\u7406\u6682\u884c\u529e\u6cd5)",
          "jurisdiction": "CN",
          "domain": "Interim Measures for the Management of Generative Artificial Intelligence Services (\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd\u670d\u52a1\u7ba1\u7406\u6682\u884c\u529e\u6cd5): binding regulation governing generative AI services publicly available in China, issued by the Cyberspace Administration of China (\u4e2d\u56fd\u7f51\u4fe1\u7f51 / \u4e2d\u592e\u7f51\u7edc\u5b89\u5168\u548c\u4fe1\u606f\u5316\u59d4\u5458\u4f1a\u529e\u516c\u5ba4), issued 10 July 2023, published 13 July 2023. Chinese-language primary instrument.",
          "track": "ai_content_governance",
          "source_url": "http://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm",
          "tier": "tier_2_primary_html",
          "note": "Fetch-backed entry. File: aigov_cn__cn-genai-interim-measures-2023.html (sha256 7fae988c, 29431 B), http_status 200, fetch_datetime_utc 2026-07-25T19:19:52.955402+00:00, content_length_chars 21791. Source is Chinese-language. Supports spans decoded from base64 content of saved file. Effective date (2023-08-15, commonly cited) was not extracted from the saved file; enforcement_date field omitted."
        },
        {
          "authority": "Cyberspace Administration of China \u2014 Measures for Labeling of AI-Generated Synthetic Content (\u4eba\u5de5\u667a\u80fd\u751f\u6210\u5408\u6210\u5185\u5bb9\u6807\u8bc6\u529e\u6cd5)",
          "jurisdiction": "CN",
          "domain": "Measures for Labeling of AI-Generated Synthetic Content (\u4eba\u5de5\u667a\u80fd\u751f\u6210\u5408\u6210\u5185\u5bb9\u6807\u8bc6\u529e\u6cd5), document number \u56fd\u4fe1\u529e\u901a\u5b57\u30142025\u30152\u53f7, dated 14 March 2025: binding regulation issued by the Cyberspace Administration of China, requiring labeling of AI-generated and AI-synthesized content; applies to internet application distribution platforms during app listing and online review (\u4e92\u8054\u7f51\u5e94\u7528\u7a0b\u5e8f\u5206\u53d1\u5e73\u53f0\u5728\u5e94\u7528\u7a0b\u5e8f\u4e0a\u67b6\u6216\u8005\u4e0a\u7ebf\u5ba1\u6838\u65f6). Chinese-language primary instrument.",
          "track": "ai_content_governance",
          "source_url": "https://www.cac.gov.cn/2025-03/14/c_1743654684782215.htm",
          "tier": "tier_2_primary_html",
          "note": "Fetch-backed entry. File: aigov_cn__cn-aigc-labeling-measures-2025.html (sha256 3686321b, 23354 B), http_status 200, fetch_datetime_utc 2026-07-25T19:19:54.859044+00:00, content_length_chars 17942. Source is Chinese-language. Supports spans decoded from base64 content of saved file."
        },
        {
          "authority": "Australian Prudential Regulation Authority (APRA)",
          "jurisdiction": "AU",
          "domain": "APRA letter to industry, 'Letter to Industry on Artificial Intelligence (AI)', published 30 April 2026, addressed to all APRA-regulated entities (banks, insurers and superannuation trustees): outlines observations from a late-2025 targeted supervisory engagement and APRA's expectations for managing AI-related risk. Warns that governance, risk management, assurance and operational resilience practices are not keeping pace with AI adoption; sets minimum Board expectations (AI literacy sufficient for effective challenge; oversight of an AI strategy consistent with risk appetite, with monitoring, reporting and defined triggers); states APRA's principle-based prudential framework is technology and vendor agnostic and requires appropriate AI risk management \u2014 risk appetite, exposure management, oversight and accountability \u2014 with stronger supervisory action and enforcement where risks are not managed proportionately. Companion media release, same date, confirms no additional requirements proposed at this stage. Supervisory letter; not a binding statute.",
          "track": "high_risk_ai_systems",
          "source_url": "https://www.apra.gov.au/apra-letter-to-industry-on-artificial-intelligence-ai",
          "tier": "tier_2_primary_html",
          "note": "Fetch-backed entry. Two files: aigov_au_apra__apra-ai-letter-to-industry.html (sha256 8177292b, 992479 B, http_status 200, fetch_datetime_utc 2026-07-25T19:19:55.625214+00:00, content_length_chars 992440) and aigov_au_apra__apra-ai-media-release-step-change.html (sha256 47eed6d4, 981907 B, http_status 200, fetch_datetime_utc 2026-07-25T19:19:56.258734+00:00, content_length_chars 981876). Extraction provenance: Operator-executed strip-tags cell in Colab against the two saved files, output pasted in Advisor session 2026-07-27; supports spans are verbatim from that output (Ruling 3)."
        },
        {
          "authority": "Australia \u2014 Department of Industry, Science and Resources (DISR) \u2014 Voluntary AI Safety Standard",
          "jurisdiction": "AU",
          "domain": "Voluntary AI Safety Standard (VAISS): non-binding guideline and procedure for implementing safe, responsible AI systems across all sectors. Implementation guidance covering six essential practices: (1) Decide who is accountable, (2) Understand impacts and plan accordingly, (3) Measure and manage risks, (4) Share essential information, (5) Test and monitor, (6) Maintain human control. Both on-disk captures state the implementation guidance 'evolves the Voluntary AI Safety Standard'.",
          "track": "all_ai_systems",
          "source_url": "https://www.industry.gov.au/publications/voluntary-ai-safety-standard",
          "tier": "tier_2_primary_html",
          "note": "Capture-backed entry. Two files: Voluntary AI Safety Standard\u2026html (sha256 014e62f1, 1225677 B) and Guidance for AI adoption\u2026pdf (sha256 1221fd00, 563597 B); both admitted per batch receipts 2026-07-26. Non-binding confirmed: VAISS HTML carries dcterms.type 'Guideline or procedure'. \u00a76.3 DISR CONFLICT (R4 \u00a76.3 amended): both on-disk captures carry the formulation 'evolves the Voluntary AI Safety Standard'; the 2026-07-24 absence log (session_fetch_path_attempts[3]) records a prior observation that 'guidance published 21 October 2025 replaces the Voluntary AI Safety Standard', from the same domain, with no file behind it. This discrepancy is recorded unreconciled, dated 2026-07-24 \u2014 no reconciliation offered per R4 \u00a76.3. Official instrument PDF (published 05 May 2026, referenced in the Guidance) is not on disk; not cited."
        }
      ]
    },
    {
      "category_id": "category_5_multilateral_standards",
      "category_title": "MULTILATERAL / INTERNATIONAL STANDARDS",
      "category_target": "Non-binding but globally influential frameworks adopted across 40+ jurisdictions. ISO 42001 is the only certifiable AI management system standard. G7 Hiroshima Code and OECD AI Principles are soft-law baselines regulators measure against.",
      "authorities": [
        {
          "authority": "ISO/IEC 42001 \u2014 AI Management System Standard",
          "jurisdiction": "international",
          "domain": "Certifiable AI management system standard. Three-year certification cycle with annual surveillance audits. Controls define roles, responsibilities, and human-in-the-loop controls. ISO 42006:2025 governs AI auditor qualification. Multinational enterprises use 42001 as a compliance passport across jurisdictions. Full RMF implementation provides 60-70% of ISO 42001 certification evidence.",
          "track": "all_ai_systems",
          "source_url": "https://www.iso.org/standard/81230.html",
          "tier": "tier_2_primary_html",
          "note": "The highest-value third-party attestation an AI governance claim can carry. If a company claims ISO 42001 certification, verify certificate validity and surveillance audit currency."
        },
        {
          "authority": "OECD AI Principles \u2014 Reporting Framework (February 2025)",
          "jurisdiction": "multilateral-46-countries",
          "domain": "Five principles: transparency, accountability, robustness, privacy, human oversight. Soft-law baseline adopted by 46 countries. OECD Reporting Framework launched February 2025; 19 companies reported by April 2025. Non-binding but increasingly the benchmark regulators measure against.",
          "track": "all_ai_systems",
          "source_url": "https://oecd.ai/en/ai-principles",
          "tier": "tier_2_primary_html"
        },
        {
          "authority": "G7 Hiroshima Code of Conduct (October 2023 / December 2023)",
          "jurisdiction": "multilateral-G7",
          "domain": "Applies to frontier AI developers. Five principles including human oversight. Endorsed by G7 leaders December 2023. Non-binding but carries weight as political commitment from G7 member regulators who separately enforce binding national frameworks.",
          "track": "all_ai_systems",
          "source_url": "https://www.g7hiroshima.go.jp/en/documents/",
          "tier": "tier_3_trend_only",
          "note": "Signal source only. Do not cite as binding rule. Cite alongside the binding frameworks in its member jurisdictions (EU AI Act, NIST, FCA)."
        }
      ]
    },
    {
      "category_id": "category_6_academic_research_signal",
      "category_title": "ACADEMIC / RESEARCH \u2014 SIGNAL ONLY",
      "category_target": "Peer-reviewed and conference-published research documenting structural limitations of AI-only oversight. Tier 3: signal and citation support only. Never the citation of record for a regulatory requirement. Use to reinforce the structural proposition when a binding authority citation is already established.",
      "authorities": [
        {
          "authority": "FAccT '26 \u2014 'Who Judges the Judges?' (arXiv 2605.24737, May 2026)",
          "jurisdiction": "academic",
          "domain": "Documents structural gap in LLM-as-a-judge compliance monitoring. Key finding: existing legal instruments were designed to neutralise pre-existing bias in training data, not to detect emergent behavioural drift post-deployment. A single undifferentiated LLM judge with no per-regulatory-article decomposition and no runtime monitoring fails regulatory requirements structurally, not just practically.",
          "track": "ai_content_governance",
          "source_url": "https://arxiv.org/abs/2605.24737",
          "tier": "tier_3_trend_only",
          "note": "FAccT '26 conference paper. Cite as supporting evidence after establishing binding authority. Do not lead with this as the primary citation."
        },
        {
          "authority": "Kiteworks \u2014 Human-in-the-Loop AI Compliance Analysis (March 2026)",
          "jurisdiction": "industry-analysis",
          "domain": "Nominal oversight is not compliant oversight \u2014 a reviewer who lacks the information, authority, or time to genuinely influence an AI decision does not satisfy meaningful human review requirements. The delegation chain is the evidentiary foundation: every AI agent action must be attributable to a human author. Covers GDPR Article 22, EU AI Act, HIPAA, financial services model risk frameworks.",
          "track": "all_ai_systems",
          "source_url": "https://www.kiteworks.com/regulatory-compliance/human-in-the-loop-ai-compliance/",
          "tier": "tier_3_trend_only",
          "note": "Strongest plain-language statement of the core proposition available. Use for reader-facing explanatory language after citing Article 14 or equivalent primary source."
        },
        {
          "authority": "Zylos Research \u2014 AI Agent Governance 2026 (May 2026)",
          "jurisdiction": "industry-analysis",
          "domain": "Model-level defenses (instruction hierarchy, system prompt hardening) are necessary but insufficient. Only access controls enforced at the data layer, independent of the model, can prevent an injected instruction from producing a compliance event. Direct structural argument: one LLM cannot police another LLM's compliance because the defense must be independent of the model layer.",
          "track": "ai_content_governance",
          "source_url": "https://zylos.ai/research/2026-05-01-ai-agent-governance-compliance-2026/",
          "tier": "tier_3_trend_only"
        }
      ]
    },
    {
      "category_id": "category_7_gulf_mena_ai_governance",
      "category_title": "GULF / MENA \u2014 AI GOVERNANCE",
      "category_target": "Gulf / Middle East AI governance frameworks. Populated at R3 with DIFC (UAE): Data Protection Regulations, Regulation 10 (in force 1 September 2023), governing personal data processed through autonomous and semi-autonomous systems, with obligations on deployers and operators. Broader UAE federal and regional frameworks remain unpopulated \u2014 see config_gaps.",
      "authorities": [
        {
          "authority": "Dubai International Financial Centre (DIFC) \u2014 Data Protection Regulations, Regulation 10",
          "jurisdiction": "UAE",
          "domain": "DIFC Data Protection Regulations (Consolidated Version No. 2, in force 1 September 2023), Regulation 10 \u2014 Personal Data Processed Through Autonomous and Semi-Autonomous Systems: governs personal data processed through autonomous and semi-autonomous systems within the DIFC; sets obligations on deployers and operators of such systems. Binding DIFC regulation. Regulation 10 text cited from the consolidated Data Protection Regulations PDF.",
          "track": "high_risk_ai_systems",
          "source_url": "https://www.difc.com/business/registrars-and-commissioners/commissioner-of-data-protection/regulation-10",
          "tier": "tier_2_primary_html",
          "enforcement_date": "2023-09-01",
          "note": "Anchor: data-protection-regulation.pdf (sha256 1b9d02b3, 479434 B), admitted per batch receipts 2026-07-26; pipeline failure reference: records [6] and [7] (aigov_ae, http_429, difc.com and difc.ae) in ai_governance_retrieval_absences_20260725.json. Per R4 \u00a75.1: Regulation 10 is cited from inside the consolidated DPR PDF, not the portal page. Supplementary context on disk: Regulation 10.pdf (sha256 1d9cdfad), difc-regulation-10-committee-charter.pdf (sha256 791178c1), DIFC Enacts Amended\u2026html (sha256 d499d10e) \u2014 supplementary context only; not citable as the instrument."
        }
      ]
    }
  ],
  "audit_usage_instructions": {
    "load_condition": "Load this config when the pre-fetch worker detects one or more claim_trigger_keywords on the target website homepage or subpages.",
    "section_d_usage": "For Section D (Regulatory/Compliance Term Accuracy) checks D1, D2, D4: when the target claims AI governance, approval-first behavior, or compliance-grade AI outputs, fetch the applicable authority entries from categories 1-5 at session time. Record the fetch URL and retrieval date. The fetched authority text is the primary source for the finding. Do not cite this config itself as the finding \u2014 it is the map, not the source.",
    "section_a_usage": "For Section A (Source Chain-of-Custody) check A1: when the target claims AI audit trail, immutable logging, or source-bound AI outputs, apply Article 12 (tamper-evident logging requirement) and ISO 42001 controls as the benchmark for what third-party substantiation would look like.",
    "resolution_framing": "Resolutions for AI governance substantiation gaps must never name a specific vendor. Frame as: the absence of a verifiable governance framework presents material substantiation risk under [cite applicable regime]. Resolution requires a documented human-in-the-loop AI content governance workflow with tamper-evident per-call audit records and operator approval gates, and a linked public disclosure of that framework, conforming to [cite applicable Article or framework function].",
    "what_this_config_cannot_do": "This config does not resolve entity structure gaps, privacy page linking gaps, competitive disparagement, or attribution hygiene issues. Those remain content-edit items. This config applies only to AI governance substantiation gaps where the root cause is absence of a verifiable governance infrastructure."
  },
  "config_gaps": [
    "HK SFC and HKMA entries populated at R3 (2026-07-27); placeholder removed per Operator ratification.",
    "Brazil AI framework not yet populated (excluded from R3 scope per Operator scoping ruling 2026-07-25). Japan, South Korea, UAE populated at R3 (2026-07-27).",
    "China generative AI identity verification (2023) not yet populated. Applicable when target serves CN users.",
    "claim_trigger_keyword detection logic does not exist in current pre-fetch worker \u2014 requires builder adjustment (see adjustment notes)."
  ],
  "disclosure_screen": {
    "_note": "Added R2. The AI-Deployment Disclosure Screen (ADS) \u2014 Executive Predicate Determination Procedure. Governs custody_predicate field in exec_issues. Not a 25th check. Evidenced from Sections D and A. See D1_ads_procedure_library_entry.json for full procedure.",
    "four_properties": {
      "P1": {
        "label": "Human review authority",
        "detection_guidance": "Search target's public pages (homepage, product pages, privacy policy, terms of service, AI disclosure page if present) for explicit naming of a human role responsible for approving AI-assisted output. Keywords that may indicate presence: 'human review,' 'analyst approval,' 'compliance officer reviews,' 'licensed [role] approves,' 'human in the loop,' 'subject to human approval.' Keywords that do NOT satisfy P1: 'AI checks,' 'automated review,' 'system validates,' 'our model verifies.' Capture verbatim text of any candidate passage with URL and fetch date.",
        "pass_signal": "Named human role (not a process or system) explicitly holds approval authority for AI-assisted output before release.",
        "absence_signal": "No named human role found on fetched pages. Or: approval authority attributed to a system, algorithm, or AI process.",
        "fail_signal": "Documentation explicitly states that AI output is released without human review, or that human review is optional or advisory only."
      },
      "P2": {
        "label": "External verification",
        "detection_guidance": "Search for descriptions of AI output being verified against a non-LLM source. Keywords that may indicate presence: 'verified against,' 'cross-referenced with,' 'confirmed from,' 'source-bound,' 'primary source verification,' 'external database,' 'regulatory feed.' Keywords that do NOT satisfy P2: 'AI-reviewed,' 'model-checked,' 'self-verified,' 'confidence score.' The verification source must be structurally independent of the generating model \u2014 a structured data feed, a registry, a human subject-matter expert review against primary source, or an equivalent non-LLM verification layer.",
        "pass_signal": "Non-LLM verification source explicitly named or described as part of the AI output workflow.",
        "absence_signal": "No non-LLM verification source described. Or: verification described only as AI-internal (model confidence, self-consistency check, AI-vs-AI).",
        "fail_signal": "Documentation explicitly states that AI output is not verified against external sources."
      },
      "P3": {
        "label": "Tamper-evident record",
        "detection_guidance": "Search for descriptions of audit logging with tamper-evident design. Keywords that may indicate presence: 'immutable audit log,' 'tamper-evident,' 'append-only,' 'cryptographically sealed,' 'chain-of-custody record,' 'third-party custodied log,' 'write-once.' Keywords that do NOT satisfy P3: 'audit log,' 'event history,' 'activity log,' 'downloadable history' \u2014 unless accompanied by tamper-evident design description. A modifiable log does not satisfy P3 regardless of retention period.",
        "pass_signal": "Tamper-evident design explicitly described for AI generation and approval event records.",
        "absence_signal": "Logging described without tamper-evident controls. Or: no logging described at all for AI-generation events.",
        "fail_signal": "Documentation explicitly describes a mutable or administrator-editable log of AI generation events."
      },
      "P4": {
        "label": "Linked disclosure",
        "detection_guidance": "From each page making an AI claim, check for: (a) a direct link to a governance or AI disclosure document; (b) a navigation path to such a document within two clicks from the AI claim page; (c) inline disclosure of controls adjacent to the AI claim. A general privacy policy footer link satisfies P4 only if the privacy policy contains the controls description AND the footer link is present on the AI claim page. An AI disclosure page accessible only from the site map or a search does not satisfy P4.",
        "pass_signal": "Direct link or clear two-click navigation from AI claim page to controls description.",
        "absence_signal": "No link or navigation path to controls description from AI claim page. Or: controls description exists but is not linked or navigable from the point of the AI claim.",
        "fail_signal": "AI claim page explicitly states that governance documentation is not publicly available."
      }
    },
    "prohibited_language": {
      "in_resolution": [
        "Never name a specific AI provider, model, or vendor in the resolution.",
        "Never claim or imply that the target is legally compliant or non-compliant with any statute or regulation.",
        "Never cite a statute as satisfied (e.g., do not write 'this would satisfy Article 14').",
        "Never use outcome language ('will ensure compliance,' 'will meet regulatory requirements').",
        "Use functional language only: what the control does, not what status it confers."
      ],
      "in_discovered_issue": [
        "Never characterize the absence as a legal violation.",
        "Never characterize the absence as intentional or deceptive.",
        "Record what was looked for, what was found, and what was absent \u2014 no inference about intent."
      ]
    },
    "approved_resolution_template": {
      "label": "Four-sentence functional statement for predicate exec_issues item resolution field",
      "template": "Publish a linked, publicly accessible AI governance statement that documents: (1) the named human role responsible for reviewing and approving AI-assisted output before release; (2) the non-AI verification source used to confirm AI output against primary records; (3) the tamper-evident or chain-of-custody record that preserves generation and approval events; and (4) a link from each AI feature label to this statement. The statement should describe functional controls, not outcomes. No specific provider needs to be named. The absent properties in this audit are: [list absent properties P1/P2/P3/P4 from Step 2].",
      "usage": "Use verbatim for the 'resolution' field of the predicate exec_issues item. Bracket content is replaced with audit-specific values. Do not paraphrase the functional properties \u2014 the four-sentence structure is load-bearing for the renderer's Zone 1 display.",
      "entity_placeholder_note": "Replace with target-entity-neutral language. Do not name the target entity in the resolution beyond what is necessary for specificity. The resolution is a work order, not a legal conclusion."
    },
    "activation": {
      "trigger_condition": "Step 1 of the ADS screen fires when any claim_trigger_keyword from this config (or any equivalent AI usage or AI-driven behavior claim) is detected on the target's public pages.",
      "sections_evidenced_from": [
        "A",
        "D"
      ],
      "checks_contributing_evidence": [
        "A1",
        "A4",
        "D1",
        "D2",
        "D4"
      ],
      "output_field": "custody_predicate on one exec_issues item (boolean, maximum one true per report)",
      "procedure_reference": "D1_ads_procedure_library_entry.json \u2014 full step-by-step procedure with classification table and evidence rules"
    }
  }
}