{ "file_id": "12_vertical_grc_esg_enterprise_risk", "version": "2026.03.16", "schema_version": "2.2", "status": "Production Authority", "last_authoritative_sync": "2026-03-16", "description": "Comprehensive enumeration library for the GRC, ESG & Enterprise Risk Management vertical. This is the capstone vertical — it provides the governance, risk, and compliance vocabulary that applies to the agentic AI GRC portal itself, and to any organisation deploying agentic AI at enterprise scale. Covers: ISO 31000:2018 / COSO ERM 2017 risk assessment and treatment lifecycle, NIST AI RMF 1.0 implementation tiers and function mapping, ISO/IEC 42001:2023 AI management system requirements, AI governance maturity modelling, third-party AI vendor risk management (SR 11-7, EU AI Act supply chain), ESG programme governance and double materiality, internal audit and assurance for AI systems (IIA Standards, ISAE 3000), enterprise risk appetite and tolerance framework, incident and near-miss management, board and executive AI governance obligations, and the regulatory cross-reference matrix linking the core SDK enumerations to the major AI governance frameworks. Designed for use as OTel span attributes in an agentic AI SDK and as policy vocabulary in an OPA Rego GRC portal.", "vertical_metadata": { "vertical_key": "grc_esg", "industry": "GRC, ESG & Enterprise Risk Management", "primary_standards": [ "ISO 31000:2018 — Risk Management Guidelines", "ISO 31010:2019 — Risk Assessment Techniques", "COSO ERM 2017 — Enterprise Risk Management — Integrating with Strategy and Performance", "COSO Internal Control — Integrated Framework (2013)", "ISO/IEC 42001:2023 — Artificial Intelligence Management System (AIMS)", "ISO/IEC 23894:2023 — Artificial Intelligence — Guidance on Risk Management", "ISO/IEC 38507:2022 — Governance of AI by the Organisation", "NIST AI RMF 1.0 — Artificial Intelligence Risk Management Framework (January 2023)", "NIST AI RMF Playbook — GOVERN, MAP, MEASURE, MANAGE function actions", "NIST AI 600-1 — Generative AI Profile (July 2024)", "NIST SP 800-30 Rev 1 — Guide for Conducting Risk Assessments", "EU AI Act (2024/1689) — Full regulatory framework including Articles 9, 10, 11, 12, 13, 14, 17, 61, 62", "EU AI Act Article 9 — Risk management system for high-risk AI", "EU AI Act Article 17 — Quality management system for providers", "EU AI Act Article 61 — Post-market monitoring by providers", "EU AI Act Article 62 — Reporting of serious incidents", "EU AI Act Articles 53–55 — GPAI model obligations and codes of practice", "EU AI Office — GPAI Model Code of Practice (Draft, March 2025)", "OECD Principles on AI (2019, updated 2024) — International AI governance reference", "G7 Hiroshima AI Process (2023) — Code of conduct for advanced AI", "Council of Europe AI Convention CETS 225 (2024)", "Federal Reserve SR 11-7 — Supervisory Guidance on Model Risk Management (2011, still current)", "OCC 2011-12 — Model Risk Management (banks)", "EBA Guidelines on Internal Governance (EBA/GL/2021/05) — AI model governance", "DORA (EU) 2022/2554 — Digital Operational Resilience Act — ICT risk management and AI", "Basel Committee BCBS 239 — Principles for Effective Risk Data Aggregation", "IIA International Standards for the Professional Practice of Internal Auditing (2024 edition)", "IIA Global Technology Audit Guide (GTAG) — Auditing Artificial Intelligence", "ISAE 3000 (Revised) — Assurance Engagements Other than Audits or Reviews", "ISSB IFRS S1 — General Requirements for Sustainability-related Financial Disclosures", "TCFD — Task Force on Climate-related Financial Disclosures (superseded by ISSB but foundational)", "GRI 101: Foundation 2021 — GRI Standards for sustainability reporting", "SASB Standards — Sector-specific ESG metrics (now under ISSB)", "ISO 14001:2015 — Environmental Management Systems", "UN Sustainable Development Goals (SDGs) — Framework for ESG alignment reporting", "SBTi (Science Based Targets initiative) — Corporate net-zero target validation", "CDP (Carbon Disclosure Project) — Climate, water, and forests disclosure" ], "primary_source_urls": [ "https://airc.nist.gov/airmf-resources/airmf/5-sec-core/", "https://www.iso.org/standard/81230.html", "https://www.coso.org/guidance-on-erm", "https://www.iso.org/standard/77304.html", "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689", "https://www.ifrs.org/issued-standards/ifrs-sustainability-standards-navigator/", "https://www.theiia.org/en/standards/", "https://www.fsb-tcfd.org/" ], "otel_namespace": "grc", "opa_namespace": "data.grc", "agentic_ai_deployment_context": "As of March 2026, agentic AI in GRC, ESG, and enterprise risk management is deployed across: automated risk assessment and risk register maintenance (ISO 31000 / COSO ERM), continuous control monitoring and automated evidence collection (NIST SP 800-53, SOX 404), AI-driven regulatory horizon scanning and obligation mapping, AI governance maturity assessment and gap analysis (ISO/IEC 42001, NIST AI RMF), third-party AI vendor risk evaluation and due diligence, AI model risk management under SR 11-7 (model inventory, validation, performance monitoring), ESG data collection, materiality assessment, and disclosure preparation (CSRD, ISSB, SEC Climate Rule), internal audit AI (AI-assisted audit planning, continuous transaction monitoring, anomaly detection), AI incident and near-miss reporting (EU AI Act Article 62, DORA), board-level AI governance reporting and dashboard generation, and enterprise AI programme governance (Chief AI Officer enablement, AI use case inventory under OMB M-24-10). ISO/IEC 42001:2023 is the emerging certification standard for AI management systems — analogous to ISO 27001 for information security — and is driving significant GRC platform investment in 2025–2026. The EU AI Act Article 9 risk management system requirement and Article 17 quality management system requirement are the most operationally demanding regulatory obligations for providers of high-risk AI, applying from August 2026.", "key_regulatory_risk_note": "ISO/IEC 42001:2023 is the first certifiable AI Management System standard. It requires organisations to establish context, define AI policy, conduct AI risk assessments, implement controls from Annex A, and undergo third-party certification audits. Several EU member state regulators and the EU AI Office are signalling that ISO 42001 certification may satisfy (or substantially support) the EU AI Act Article 17 quality management system requirements for providers of high-risk AI. The EU AI Act Article 62 serious incident reporting obligation requires providers to report serious incidents (death, serious harm, fundamental rights violations) to market surveillance authorities without undue delay — AI GRC platforms must build automated incident classification and regulatory notification workflows. DORA Article 28 requires EU financial entities to maintain a register of all ICT service providers, including AI vendors — AI GRC portals are the natural home for this register. SR 11-7 model risk management guidance, while US banking-focused, has become the de facto global standard for AI model governance in financial services and is increasingly applied by non-financial regulators by analogy." }, "subdomains": [ { "subdomain": "Enterprise Risk Assessment & Treatment", "description": "Covers ISO 31000:2018, ISO 31010:2019, COSO ERM, and NIST SP 800-30 enumerations for risk assessment methodology, risk treatment strategy, risk appetite, and risk register lifecycle management. AI risk assessment agents use these values to maintain the enterprise risk register and generate board-level risk reporting.", "relevant_standards": [ "ISO 31000:2018 — Risk Management Guidelines", "ISO 31010:2019 — Risk Assessment Techniques (42 techniques catalogued)", "COSO ERM 2017 — Enterprise Risk Management Framework", "NIST SP 800-30 Rev 1 — Guide for Conducting Risk Assessments", "ISO/IEC 27005:2022 — Guidance on Managing Information Security Risks" ], "categories": [ { "enum_name": "RiskAssessmentMethod", "label": "Risk Assessment Method", "otel_attribute": "grc.risk.assessment_method", "opa_policy_path": "data.grc.risk.assessment_method", "rego_input_key": "grc_risk_assessment_method", "stability": "stable", "description": "Risk assessment methodology applied by an AI risk assessment agent. ISO 31010:2019 catalogs 42 risk assessment techniques — this enum covers the most commonly applied methods in enterprise AI risk programmes. The chosen method must be proportionate to the risk complexity and documented in the risk assessment record.", "permitted_values": [ "qualitative", "quantitative", "semi_quantitative", "bow_tie_analysis", "fmea_failure_modes", "threat_modeling_stride", "red_team_exercise", "automated_scan_vulnerability", "scenario_analysis", "monte_carlo_simulation", "fault_tree_analysis", "bayesian_network", "control_self_assessment", "delphi_expert_elicitation" ], "value_labels": { "qualitative": "Qualitative", "quantitative": "Quantitative", "semi_quantitative": "Semi-Quantitative", "bow_tie_analysis": "Bow-Tie Analysis", "fmea_failure_modes": "FMEA (Failure Modes and Effects Analysis)", "threat_modeling_stride": "Threat Modeling (STRIDE)", "red_team_exercise": "Red Team Exercise", "automated_scan_vulnerability": "Automated Vulnerability Scan", "scenario_analysis": "Scenario Analysis", "monte_carlo_simulation": "Monte Carlo Simulation", "fault_tree_analysis": "Fault Tree Analysis", "bayesian_network": "Bayesian Network", "control_self_assessment": "Control Self-Assessment", "delphi_expert_elicitation": "Delphi Expert Elicitation" }, "code_definitions": { "bow_tie_analysis": "ISO 31010 Technique 15 — maps threat sources through events to consequences; visualises both preventive and recovery controls", "fmea_failure_modes": "ISO 31010 Technique 4 — Failure Mode and Effects Analysis; systematic identification of ways in which components or processes can fail", "threat_modeling_stride": "STRIDE model (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege); AI threat modelling for system design", "red_team_exercise": "Adversarial testing by an independent team attempting to identify vulnerabilities, exploits, or unexpected model behaviours; required for high-risk AI under EU AI Act Article 9", "monte_carlo_simulation": "Quantitative risk aggregation via probabilistic simulation; used for operational risk capital modelling and risk appetite quantification" }, "regulatory_mappings": { "eu_ai_act_art9": "EU AI Act Article 9 — High-risk AI risk management system requires documented risk identification and evaluation; the chosen risk assessment method must be recorded and reproducible", "nist_ai_rmf_map": "NIST AI RMF MAP function — Identify and analyse AI risks; MAP 1.1 requires documented risk assessment methodology", "iso_42001_clause6_1": "ISO/IEC 42001 Clause 6.1.2 — AI risk assessment must use a defined methodology producing comparable and reproducible results" }, "use_case": "AI GRC agent selects risk assessment method based on risk category and materiality. Tier 4 critical AI systems require at minimum 'red_team_exercise' and 'threat_modeling_stride'. OPA policy enforces that any new high-risk AI deployment must have a completed risk assessment with a documented method before receiving production ATO.", "source": "ISO 31010:2019; NIST SP 800-30 Rev 1; ISO/IEC 42001 Clause 6.1.2; EU AI Act Article 9", "source_url": "https://www.iso.org/standard/72140.html" }, { "enum_name": "RiskTreatmentStrategy", "label": "Risk Treatment Strategy", "otel_attribute": "grc.risk.treatment_strategy", "opa_policy_path": "data.grc.risk.treatment_strategy", "rego_input_key": "grc_risk_treatment_strategy", "stability": "stable", "description": "ISO 31000:2018 Section 6.5 risk treatment option. AI risk management agents apply treatment strategies to identified risks. Treatment decisions for residual risks above the organisation's risk appetite threshold must be escalated to the Risk Committee — AI may not autonomously accept above-threshold residual risks.", "permitted_values": [ "avoid", "reduce_mitigate", "share_transfer", "accept_retain", "monitor_watch_list", "exploit_opportunity" ], "value_labels": { "avoid": "Avoid", "reduce_mitigate": "Reduce Mitigate", "share_transfer": "Share Transfer", "accept_retain": "Accept Retain", "monitor_watch_list": "Monitor Watch List", "exploit_opportunity": "COSO ERM Positive Risk" }, "code_definitions": { "avoid": "Eliminate the activity or condition giving rise to the risk; most conservative treatment; appropriate where risk cannot be adequately mitigated", "reduce_mitigate": "Apply controls to reduce likelihood, consequence, or both; most common treatment for operational AI risks", "share_transfer": "Contractually transfer or share the risk with a third party (insurance, vendor indemnity, outsourcing); does not eliminate the risk from the organisation's perspective", "accept_retain": "Consciously accept the risk without further treatment; requires explicit authorisation by the risk owner and Risk Committee for above-appetite risks", "monitor_watch_list": "Risk is below current appetite threshold but is being tracked for change; no active treatment applied", "exploit_opportunity": "COSO ERM positive risk — take action to increase the probability of a beneficial outcome; applicable to AI adoption opportunity risks" }, "regulatory_mappings": { "eu_ai_act_art9": "EU AI Act Article 9(3) — Risk management system must include residual risk evaluation; 'accept_retain' for any risk classified as high requires documented AO or Risk Committee approval", "iso_31000_6_5": "ISO 31000:2018 Section 6.5 — Risk treatment; treatment plan must be documented with risk owner, timeline, and monitoring arrangements", "sr_11_7": "SR 11-7 — Model risk management: 'accept_retain' treatment for model limitations requires documented management approval and enhanced monitoring" }, "use_case": "AI risk agent assigns a treatment strategy to each identified risk. OPA policy blocks 'accept_retain' treatment for any AI risk rated above the board-approved risk appetite threshold without explicit Risk Committee or CRO HITL authorisation.", "source": "ISO 31000:2018 Section 6.5 — Risk Treatment Options", "source_url": "https://www.iso.org/standard/65694.html" }, { "enum_name": "RiskAppetiteStatus", "label": "Risk Appetite Status", "otel_attribute": "grc.risk.appetite_status", "opa_policy_path": "data.grc.risk.appetite_status", "rego_input_key": "grc_risk_appetite_status", "stability": "stable", "description": "Classification of a risk's current position relative to the board-approved risk appetite and tolerance thresholds. COSO ERM 2017 defines risk appetite as the amount of risk an entity is willing to accept in pursuit of value. AI GRC agents flag breaches in real time.", "permitted_values": [ "within_appetite", "approaching_tolerance", "within_tolerance_above_appetite", "exceeds_tolerance", "board_exception_approved", "appetite_not_defined" ], "value_labels": { "within_appetite": "Within Appetite", "approaching_tolerance": "Approaching Tolerance", "within_tolerance_above_appetite": "Within Tolerance Above Appetite", "exceeds_tolerance": "Exceeds Tolerance", "board_exception_approved": "Board Exception Approved", "appetite_not_defined": "Appetite Not Defined" }, "code_definitions": { "within_appetite": "Risk exposure is within the board-approved risk appetite band; no escalation required", "approaching_tolerance": "Risk exposure is trending toward the risk tolerance ceiling; risk owner alert triggered; treatment review initiated", "within_tolerance_above_appetite": "Risk exposure is above appetite but within the maximum tolerance; formal risk treatment plan and senior management notification required", "exceeds_tolerance": "Risk exposure has breached the risk tolerance ceiling; immediate board or Risk Committee escalation required; operations may need to be suspended", "board_exception_approved": "Risk exceeds normal tolerance but has been approved as a specific exception by the board or Risk Committee; time-limited with mandatory review date" }, "regulatory_mappings": { "coso_erm_2017": "COSO ERM 2017 Principle 6 — Board defines risk oversight and appetite; 'exceeds_tolerance' requires board notification", "eu_ai_act_art9": "EU AI Act Article 9 — Residual risks that exceed acceptable levels must prevent market release of high-risk AI systems", "dora_art6": "DORA Article 6 — ICT risk tolerance levels must be defined and monitored; 'exceeds_tolerance' triggers DORA ICT risk incident assessment" }, "use_case": "AI risk monitoring agent continuously recalculates risk scores. 'Exceeds_tolerance' status on any material risk triggers immediate CIRO/CRO HITL notification and initiates the board escalation workflow. AI agent cannot autonomously resolve an 'exceeds_tolerance' status — only the Risk Committee can.", "source": "COSO ERM 2017 — Risk appetite and tolerance framework; ISO 31000:2018 Section 6.3", "source_url": "https://www.coso.org/guidance-on-erm" }, { "enum_name": "ComplianceActionStatus", "label": "Compliance Action Status", "otel_attribute": "grc.compliance.action_status", "opa_policy_path": "data.grc.compliance.action_status", "rego_input_key": "grc_compliance_action_status", "stability": "stable", "description": "Status of a compliance remediation action or finding response in the GRC platform. AI compliance agents track actions through this lifecycle and escalate overdue items. Escalation to regulator status is irreversible — it must be set by a human compliance officer, not autonomously by AI.", "permitted_values": [ "open_not_started", "in_flight", "overdue", "pending_verification", "remediated", "verified_closed", "exception_granted", "exception_expired", "waived", "escalated_to_management", "escalated_to_regulator", "regulatory_breach_confirmed" ], "value_labels": { "open_not_started": "Open Not Started", "in_flight": "In Flight", "overdue": "Overdue", "pending_verification": "Pending Verification", "remediated": "Remediated", "verified_closed": "Verified Closed", "exception_granted": "Exception Granted", "exception_expired": "Exception Expired", "waived": "Waived", "escalated_to_management": "Escalated to Management", "escalated_to_regulator": "Escalated to Regulator", "regulatory_breach_confirmed": "Regulatory Breach Confirmed" }, "code_definitions": { "escalated_to_regulator": "Compliance failure has been voluntarily disclosed to the relevant regulator; regulatory response pending; legal hold in effect", "regulatory_breach_confirmed": "Regulator has confirmed a compliance breach; enforcement action risk elevated; legal counsel engagement mandatory", "exception_expired": "A previously granted exception has lapsed without renewal; the control gap is now re-open; treat as 'open_not_started' for remediation purposes" }, "regulatory_mappings": { "eu_ai_act_art62": "EU AI Act Article 62 — Serious incident reporting: 'escalated_to_regulator' status for AI serious incidents must be filed without undue delay", "dora_art19": "DORA Article 19 — ICT-related incident reporting: 'escalated_to_regulator' status for major DORA incidents within prescribed timelines", "iso_37301": "ISO 37301:2021 — Compliance management: 'regulatory_breach_confirmed' requires root cause analysis and preventive action documentation" }, "use_case": "AI compliance agent tracks all open remediation actions against their deadlines. 'Overdue' items trigger management escalation. OPA policy enforces that 'escalated_to_regulator' and 'regulatory_breach_confirmed' status transitions require CCO and General Counsel HITL — AI cannot self-escalate to regulatory authorities.", "source": "ISO 37301:2021 compliance action management; CoreStream / ServiceNow GRC action status taxonomy", "source_url": "https://www.iso.org/standard/79425.html" } ] }, { "subdomain": "NIST AI RMF & AI Governance Maturity", "description": "Covers NIST AI RMF 1.0 implementation tier, function, and subcategory enumerations, plus AI governance maturity levels for AI programme assessment. These enums are the vocabulary for AI governance dashboards, maturity assessments, and gap analysis reports.", "relevant_standards": [ "NIST AI RMF 1.0 (January 2023) — GOVERN, MAP, MEASURE, MANAGE functions", "NIST AI RMF Playbook — Action subcategories for each function", "NIST AI 600-1 — Generative AI Profile (July 2024)", "ISO/IEC 42001:2023 — AI Management System", "ISO/IEC 23894:2023 — AI Risk Management Guidance", "CMMI — Capability Maturity Model Integration (maturity model reference)" ], "categories": [ { "enum_name": "NISTAIRMFFunction", "label": "NIST AI RMF Function", "otel_attribute": "grc.nist_ai_rmf.function", "opa_policy_path": "data.grc.nist_ai_rmf.function", "rego_input_key": "grc_nist_ai_rmf_function", "stability": "stable", "description": "NIST AI RMF 1.0 core function. The four functions structure the AI risk management lifecycle from governance through active management. AI GRC agents tag every AI risk management action with the applicable function for maturity measurement and gap analysis.", "permitted_values": [ "GOVERN", "MAP", "MEASURE", "MANAGE" ], "value_labels": { "GOVERN": "Govern", "MAP": "Map", "MEASURE": "Measure", "MANAGE": "Manage" }, "code_definitions": { "GOVERN": "Cultivating and implementing AI risk management practices, policies, processes, and culture across the organisation. Precondition for the other three functions.", "MAP": "Identifying and classifying AI risks relevant to a specific AI system or use case. Context-setting, risk identification, and risk categorisation.", "MEASURE": "Analysing, assessing, and tracking AI risks using quantitative and qualitative tools. Includes bias testing, red teaming, performance monitoring, and impact assessment.", "MANAGE": "Prioritising and acting on identified AI risks. Includes treatment planning, incident response, model retirement, and residual risk acceptance." }, "source": "NIST AI RMF 1.0 — Core Functions (January 2023)", "source_url": "https://airc.nist.gov/airmf-resources/airmf/5-sec-core/" }, { "enum_name": "NISTAIRMFImplementationTier", "label": "NIST AI RMF Implementation Tier", "otel_attribute": "grc.nist_ai_rmf.implementation_tier", "opa_policy_path": "data.grc.nist_ai_rmf.implementation_tier", "rego_input_key": "grc_nist_ai_rmf_implementation_tier", "stability": "stable", "description": "NIST AI RMF 1.0 Implementation Tier characterising an organisation's AI risk management posture. Tiers reflect the degree to which AI risk management is institutionalised and integrated with enterprise risk management. Tier is assessed separately for each NIST AI RMF function.", "permitted_values": [ "tier_1_partial", "tier_2_risk_informed", "tier_3_repeatable", "tier_4_adaptive" ], "value_labels": { "tier_1_partial": "Tier 1 — Partial", "tier_2_risk_informed": "Tier 2 — Risk Informed", "tier_3_repeatable": "Tier 3 — Repeatable", "tier_4_adaptive": "Tier 4 — Adaptive" }, "code_definitions": { "tier_1_partial": "AI risk management practices are ad hoc, reactive, and not consistently applied. Limited awareness of AI risk at organisational level. No formal policy or process.", "tier_2_risk_informed": "AI risk management practices are defined but not organisation-wide. Risk-informed decisions are made but not consistently applied. Limited integration with enterprise risk management.", "tier_3_repeatable": "AI risk management practices are formally documented, consistently applied, and regularly reviewed. Integrated with enterprise risk management. Staff are trained.", "tier_4_adaptive": "AI risk management practices are continuously improved based on lessons learned and emerging risks. Adaptive to new AI technologies and threat landscape. Mature governance and culture." }, "ordered": true, "value_ordinals": { "tier_1_partial": 1, "tier_2_risk_informed": 2, "tier_3_repeatable": 3, "tier_4_adaptive": 4 }, "regulatory_mappings": { "nist_ai_rmf_1_0": "NIST AI RMF 1.0 — Implementation Tiers characterise organisational risk management posture; target tier should align with risk profile and regulatory environment", "eu_ai_act_art17": "EU AI Act Article 17 — Quality management system for providers of high-risk AI; Tier 3 or above is a reasonable proxy for Article 17 compliance posture", "iso_42001": "ISO/IEC 42001 — Third-party certification implies at least Tier 3 implementation for the certified scope" }, "source": "NIST AI RMF 1.0 — Implementation Tiers (Section 2.5)", "source_url": "https://airc.nist.gov/airmf-resources/airmf/5-sec-core/" }, { "enum_name": "AIGovernanceMaturityLevel", "label": "AI Governance Maturity Level", "otel_attribute": "grc.ai_governance.maturity_level", "opa_policy_path": "data.grc.ai_governance.maturity_level", "rego_input_key": "grc_ai_governance_maturity_level", "stability": "stable", "description": "AI governance programme maturity level. Adapted from CMMI and ISO/IEC 42001 Annex B maturity guidance. Used by AI governance assessment agents to produce maturity scorecards and roadmaps. Level 3 or above is the minimum expected for organisations deploying high-risk AI.", "permitted_values": [ "level_0_ad_hoc", "level_1_aware", "level_2_defined", "level_3_managed", "level_4_optimised", "level_5_transformative" ], "value_labels": { "level_0_ad_hoc": "Level 0 — Ad Hoc", "level_1_aware": "Level 1 — Aware", "level_2_defined": "Level 2 — Defined", "level_3_managed": "Level 3 — Managed", "level_4_optimised": "Level 4 — Optimised", "level_5_transformative": "Level 5 — Transformative" }, "code_definitions": { "level_0_ad_hoc": "No AI governance programme. AI deployed without formal risk assessment, policies, or oversight structures. Highest risk posture.", "level_1_aware": "Organisation is aware of AI governance requirements. Initial policy drafts exist. AI inventory is incomplete. No systematic risk assessment process.", "level_2_defined": "AI governance policies are documented and approved. AI inventory is maintained. Risk assessment process defined. Roles and responsibilities assigned (CAIO, AI Risk Owner).", "level_3_managed": "AI governance is systematically applied. AI risk assessments are conducted and tracked. Bias audits are performed. HITL mechanisms are implemented. Metrics are collected.", "level_4_optimised": "AI governance is continuously improved. Quantitative performance metrics drive governance decisions. Advanced bias testing and red teaming. Proactive regulatory engagement.", "level_5_transformative": "AI governance is a strategic differentiator. Real-time risk monitoring. AI ethics embedded in culture. Industry leadership on governance standards. Certified under ISO/IEC 42001." }, "ordered": true, "value_ordinals": { "level_0_ad_hoc": 1, "level_1_aware": 2, "level_2_defined": 3, "level_3_managed": 4, "level_4_optimised": 5, "level_5_transformative": 6 }, "regulatory_mappings": { "eu_ai_act_art9_17": "EU AI Act Articles 9 and 17 — Risk management and quality management systems for high-risk AI providers; Level 3 minimum required", "omb_m_24_10": "OMB M-24-10 — Federal agency AI governance; Level 2 minimum for all agencies; Level 3 for rights-impacting AI", "iso_42001": "ISO/IEC 42001 certification audit implies Level 4 or above for certified scope" }, "use_case": "AI governance assessment agent scores the organisation's maturity level across multiple dimensions. OPA policy enforces that any third-party AI vendor deploying high-risk AI into an organisation's environment must achieve at least 'level_2_defined' before integration is approved, and 'level_3_managed' for Tier A critical dependencies.", "source": "NIST AI RMF 1.0 Implementation Tiers; ISO/IEC 42001 Annex B; CMMI maturity levels", "source_url": "https://www.iso.org/standard/81230.html" } ] }, { "subdomain": "ISO/IEC 42001 AI Management System", "description": "Covers ISO/IEC 42001:2023 clause structure and conformity assessment enumerations. ISO 42001 is the certifiable AI management system standard — analogous to ISO 27001 for information security. AI GRC platforms are building 42001 compliance modules to support certification audits.", "relevant_standards": [ "ISO/IEC 42001:2023 — Artificial Intelligence Management System (AIMS)", "ISO/IEC 42001 Annex A — Controls for AI management systems", "ISO/IEC 42001 Annex B — Guidance on implementing AI controls", "ISO/IEC 42001 Annex C — Guidance on AI risk management", "ISO/IEC 42001 Annex D — Using ISO/IEC 42001 with ISO/IEC 27001", "ISO 19011:2018 — Guidelines for Auditing Management Systems" ], "categories": [ { "enum_name": "ISO42001ClauseArea", "label": "ISO 42001 Clause Area", "otel_attribute": "grc.iso42001.clause_area", "opa_policy_path": "data.grc.iso42001.clause_area", "rego_input_key": "grc_iso42001_clause_area", "stability": "stable", "description": "ISO/IEC 42001:2023 clause area for AI management system requirements. AI compliance agents tag evidence and gap findings with the applicable clause to support third-party certification audits and internal readiness assessments.", "permitted_values": [ "clause_4_context_of_organisation", "clause_5_leadership", "clause_6_planning", "clause_7_support", "clause_8_operation", "clause_9_performance_evaluation", "clause_10_improvement", "annex_a_controls", "annex_b_implementation_guidance", "annex_c_ai_risk_management" ], "value_labels": { "clause_4_context_of_organisation": "Clause 4 Context of Organisation", "clause_5_leadership": "Clause 5 Leadership", "clause_6_planning": "Clause 6 Planning", "clause_7_support": "Clause 7 Support", "clause_8_operation": "Clause 8 Operation", "clause_9_performance_evaluation": "Clause 9 Performance Evaluation", "clause_10_improvement": "Clause 10 Improvement", "annex_a_controls": "Annex A Controls", "annex_b_implementation_guidance": "Annex B Implementation Guidance", "annex_c_ai_risk_management": "Annex C AI Risk Management" }, "code_definitions": { "clause_4_context_of_organisation": "Understanding internal and external context; identifying interested parties; determining AIMS scope; AI policy alignment", "clause_5_leadership": "Top management commitment; AI policy; organisational roles and responsibilities including AI responsibility assignment", "clause_6_planning": "AI risk and opportunity assessment; AI objectives and planning; AI impact assessment", "clause_7_support": "Resources; competence; awareness; communication; documented information", "clause_8_operation": "Operational planning and control; AI system impact assessment; AI system design and development; AI system deployment and operation; third-party AI components", "clause_9_performance_evaluation": "Monitoring, measurement, analysis, and evaluation; internal audit; management review", "clause_10_improvement": "Nonconformity and corrective action; continual improvement", "annex_a_controls": "Normative controls for AI management including: policies for AI (A.2), internal organisation (A.3), resources for AI systems (A.4), AI system impact assessment (A.5), AI systems lifecycle (A.6), data for AI systems (A.7), information for interested parties (A.8), use of AI systems (A.9), third-party and customer relationships (A.10)" }, "use_case": "AI compliance agent maps every GRC action, control, and evidence record to its ISO 42001 clause area. Gap analysis report shows completeness by clause area against certification readiness. OPA policy enforces that deployment of any new AI system requires completion of Clause 8 operational planning and Annex A.5 (AI system impact assessment) before go-live.", "source": "ISO/IEC 42001:2023 — Clause structure and Annexes A–D", "source_url": "https://www.iso.org/standard/81230.html" }, { "enum_name": "AIImpactAssessmentOutcome", "label": "AI Impact Assessment Outcome", "otel_attribute": "grc.ai_impact_assessment.outcome", "opa_policy_path": "data.grc.ai_impact_assessment.outcome", "rego_input_key": "grc_ai_impact_assessment_outcome", "stability": "stable", "description": "Outcome of an AI impact assessment conducted under ISO/IEC 42001 Annex A.5 or EU AI Act Article 9 risk management system. Determines whether the AI system may proceed to deployment, requires additional controls, or must be modified or abandoned.", "permitted_values": [ "low_impact_approved", "moderate_impact_controls_required", "high_impact_enhanced_governance_required", "critical_impact_board_approval_required", "unacceptable_impact_deployment_blocked", "assessment_in_progress", "assessment_not_conducted", "reassessment_triggered_by_change" ], "value_labels": { "low_impact_approved": "Low Impact Approved", "moderate_impact_controls_required": "Moderate Impact Controls Required", "high_impact_enhanced_governance_required": "High Impact Enhanced Governance Required", "critical_impact_board_approval_required": "Critical Impact Board Approval Required", "unacceptable_impact_deployment_blocked": "Unacceptable Impact Deployment Blocked", "assessment_in_progress": "Assessment in Progress", "assessment_not_conducted": "Assessment Not Conducted", "reassessment_triggered_by_change": "Reassessment Triggered by Change" }, "code_definitions": { "unacceptable_impact_deployment_blocked": "Impact assessment identifies risks that cannot be adequately mitigated; AI system must be redesigned, restricted in scope, or abandoned; EU AI Act Article 9 residual risk test failed", "critical_impact_board_approval_required": "Impact assessment identifies material risks requiring board-level risk acceptance; AI system may only be deployed with explicit board or Risk Committee approval and documented rationale", "reassessment_triggered_by_change": "A significant change to the AI system (model update, data source change, deployment scope expansion) has triggered a mandatory reassessment of the impact assessment; system continues operating under prior assessment pending completion" }, "regulatory_mappings": { "eu_ai_act_art9": "EU AI Act Article 9(2)(b) — Risk management system must identify and evaluate residual risks after control implementation; 'unacceptable_impact_deployment_blocked' means market release is not permitted", "iso_42001_annex_a5": "ISO/IEC 42001 Annex A.5 — AI system impact assessment is a normative control; must be conducted before deployment", "gdpr_art35": "GDPR Article 35 — DPIA is a specific form of impact assessment for personal data processing; both may be required for AI systems processing personal data" }, "use_case": "OPA policy blocks production deployment of any AI system where ai_impact_assessment_outcome is 'assessment_not_conducted', 'assessment_in_progress', or 'unacceptable_impact_deployment_blocked'. 'Critical_impact_board_approval_required' requires documented board resolution before the deployment gate opens.", "source": "ISO/IEC 42001:2023 Annex A.5; EU AI Act Article 9; GDPR Article 35", "source_url": "https://www.iso.org/standard/81230.html" } ] }, { "subdomain": "Third-Party AI Risk Management", "description": "Covers SR 11-7 model risk management, EU AI Act supply chain obligations, and DORA ICT third-party risk enumerations for AI vendor due diligence and ongoing monitoring. Third-party AI risk is the fastest-growing category of AI GRC work as enterprises embed foundation models and AI SaaS platforms.", "relevant_standards": [ "Federal Reserve SR 11-7 — Supervisory Guidance on Model Risk Management (2011)", "OCC 2011-12 — Model Risk Management", "EU AI Act Articles 25, 28, 29 — Obligations of importers, distributors, and deployers in AI supply chain", "EU AI Act Article 53 — Obligations of GPAI model providers", "DORA Article 28 — General principles for managing ICT third-party risk", "DORA Article 30 — Key contractual provisions for ICT service providers", "NIST AI RMF GOVERN 6.1 — Third-party AI risk management", "ISO/IEC 42001 Annex A.10 — Third-party and customer relationships" ], "categories": [ { "enum_name": "ThirdPartyAIRiskTier", "label": "Third Party AI Risk Tier", "otel_attribute": "grc.third_party_ai.risk_tier", "opa_policy_path": "data.grc.third_party_ai.risk_tier", "rego_input_key": "grc_third_party_ai_risk_tier", "stability": "stable", "description": "Risk tier classification for a third-party AI vendor or AI model provider. Drives due diligence depth, contractual requirements, and ongoing monitoring intensity. SR 11-7 model risk management principles apply to all material AI models, regardless of whether they are built or bought.", "permitted_values": [ "tier_a_critical_dependency", "tier_b_significant", "tier_c_moderate", "tier_d_low", "not_assessed" ], "value_labels": { "tier_a_critical_dependency": "Tier A — Critical Dependency", "tier_b_significant": "Tier B — Significant", "tier_c_moderate": "Tier C — Moderate", "tier_d_low": "Tier D — Low", "not_assessed": "Not Assessed" }, "code_definitions": { "tier_a_critical_dependency": "Third-party AI vendor whose failure, compromise, or discontinuation would cause material operational disruption, regulatory breach, or financial harm. Requires: pre-engagement SOC 2 Type II + penetration test review, contractual audit rights, annual on-site assessment, exit strategy tested. EU AI Act Article 28 and DORA Article 28 Tier 1 equivalent.", "tier_b_significant": "Third-party AI vendor whose failure would cause significant but manageable disruption. Requires: pre-engagement questionnaire and SOC 2 review, contractual SLAs and audit rights, annual remote assessment.", "tier_c_moderate": "Third-party AI component with limited operational dependency. Requires: standard vendor questionnaire, contractual data processing agreement, biennial review.", "tier_d_low": "Third-party AI tool with no access to sensitive data and no operational dependency. Requires: standard procurement due diligence only.", "not_assessed": "Third-party AI vendor has been identified but not yet assessed; must not be used for production AI workloads until tier classification is completed" }, "regulatory_mappings": { "sr_11_7": "SR 11-7 — Model risk management applies to all material models including vendor models; 'tier_a_critical_dependency' and 'tier_b_significant' vendors require SR 11-7 equivalent validation", "eu_ai_act_art28": "EU AI Act Article 28 — Deployers who modify an AI system's purpose become providers; Article 25 — Distributors and importers obligations", "dora_art28": "DORA Article 28 — ICT third-party service provider register; critical ICT providers are subject to DORA oversight; 'tier_a_critical_dependency' maps to DORA critical ICT provider designation", "nist_ai_rmf_govern_6_1": "NIST AI RMF GOVERN 6.1 — Third-party AI risk management policies and practices" }, "use_case": "AI vendor risk management agent classifies every AI vendor in the third-party inventory. OPA policy blocks onboarding of any 'not_assessed' vendor for production AI workloads. 'Tier_a_critical_dependency' vendors require annual CISO-level review sign-off for continued use.", "source": "SR 11-7 model risk management; DORA Article 28; EU AI Act Articles 25 and 28; NIST AI RMF GOVERN 6.1", "source_url": "https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm" }, { "enum_name": "ModelValidationStatus", "label": "Model Validation Status", "otel_attribute": "grc.model_validation.status", "opa_policy_path": "data.grc.model_validation.status", "rego_input_key": "grc_model_validation_status", "stability": "stable", "description": "SR 11-7 / OCC 2011-12 model validation lifecycle status. Applicable to all material AI models — including foundation models deployed via API, fine-tuned models, and internally developed models. A model in production without a completed validation is an SR 11-7 finding.", "permitted_values": [ "pre_validation_development", "initial_validation_in_progress", "initial_validation_completed_approved", "initial_validation_completed_conditional_approval", "initial_validation_failed_blocked", "in_production_monitoring", "ongoing_validation_in_progress", "material_change_triggered_revalidation", "performance_degradation_triggered_review", "validation_overdue", "model_retired" ], "value_labels": { "pre_validation_development": "Pre Validation Development", "initial_validation_in_progress": "Initial Validation in Progress", "initial_validation_completed_approved": "Initial Validation Completed Approved", "initial_validation_completed_conditional_approval": "Initial Validation Completed Conditional Approval", "initial_validation_failed_blocked": "Initial Validation Failed Blocked", "in_production_monitoring": "In Production Monitoring", "ongoing_validation_in_progress": "Ongoing Validation in Progress", "material_change_triggered_revalidation": "Material Change Triggered Revalidation", "performance_degradation_triggered_review": "Performance Degradation Triggered Review", "validation_overdue": "Validation Overdue", "model_retired": "Model Retired" }, "code_definitions": { "initial_validation_completed_conditional_approval": "Model approved for production use with documented limitations and compensating controls; conditions must be tracked and resolved within defined timeframe", "material_change_triggered_revalidation": "A material change (model update, data drift, scope expansion, regulatory change) has triggered mandatory revalidation; model may continue in production under enhanced monitoring pending revalidation completion", "validation_overdue": "Scheduled periodic validation has not been completed on time; heightened monitoring required; CIRO/CRO notification triggered; models in financial services regulated entities are SR 11-7 non-compliant" }, "regulatory_mappings": { "sr_11_7": "SR 11-7 Section III — Model validation: all models must be validated before use and on a periodic ongoing basis; findings must be logged and remediated", "eu_ai_act_art9": "EU AI Act Article 9(4) — High-risk AI risk management system must include testing and evaluation; 'initial_validation_failed_blocked' is the correct outcome when testing fails Article 9(4) criteria" }, "use_case": "AI model risk agent tracks validation status for every model in the enterprise AI inventory. OPA policy blocks deployment of any model with status 'pre_validation_development', 'initial_validation_failed_blocked', or 'validation_overdue'. 'Material_change_triggered_revalidation' models receive enhanced runtime monitoring flags.", "source": "Federal Reserve SR 11-7; OCC 2011-12 model risk management lifecycle", "source_url": "https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm" }, { "enum_name": "GPAIModelObligationStatus", "label": "GPAI Model Obligation Status", "otel_attribute": "grc.gpai.obligation_status", "opa_policy_path": "data.grc.gpai.obligation_status", "rego_input_key": "grc_gpai_obligation_status", "stability": "experimental", "description": "EU AI Act Articles 53–55 compliance status for a General-Purpose AI (GPAI) model or GPAI model with systemic risk. Organisations deploying GPAI models (including GPT-4, Claude, Gemini, Llama) as components of their products must verify the upstream GPAI provider has fulfilled these obligations.", "permitted_values": [ "technical_documentation_published", "copyright_summary_published", "training_data_summary_available", "eu_copyright_policy_compliant", "systemic_risk_model_adversarial_tested", "systemic_risk_model_incident_reporting_active", "code_of_practice_signatory", "obligations_not_yet_assessed", "obligations_non_compliant" ], "value_labels": { "technical_documentation_published": "Technical Documentation Published", "copyright_summary_published": "Copyright Summary Published", "training_data_summary_available": "Training Data Summary Available", "eu_copyright_policy_compliant": "EU Copyright Policy Compliant", "systemic_risk_model_adversarial_tested": "EU AI Act Article 55(1)(a)", "systemic_risk_model_incident_reporting_active": "Systemic Risk Model Incident Reporting Active", "code_of_practice_signatory": "Code of Practice Signatory", "obligations_not_yet_assessed": "Obligations not Yet Assessed", "obligations_non_compliant": "Obligations Non Compliant" }, "code_definitions": { "systemic_risk_model_adversarial_tested": "EU AI Act Article 55(1)(a) — GPAI models with systemic risk (> 10^25 FLOPs training compute threshold) must conduct adversarial testing and red teaming; provider has published test results", "code_of_practice_signatory": "GPAI model provider has signed the EU AI Office GPAI Code of Practice (draft March 2025); compliance with the Code creates a presumption of conformity with Article 53 obligations" }, "regulatory_mappings": { "eu_ai_act_art53": "EU AI Act Article 53 — GPAI model providers must prepare technical documentation, copyright summary, and provide downstream deployers with necessary information", "eu_ai_act_art55": "EU AI Act Article 55 — Additional obligations for GPAI models with systemic risk: adversarial testing, incident reporting, cybersecurity measures, energy consumption reporting", "eu_ai_office_code": "EU AI Office GPAI Code of Practice (Draft March 2025) — Voluntary compliance creates presumption of conformity with Articles 53–55" }, "source": "EU AI Act Articles 53–55; EU AI Office GPAI Code of Practice Draft (March 2025)", "source_url": "https://digital-strategy.ec.europa.eu/en/policies/ai-office" } ] }, { "subdomain": "AI Incident Management & Post-Market Monitoring", "description": "Covers EU AI Act Article 62 serious incident reporting, NIST AI RMF MANAGE function incident lifecycle, and post-market monitoring enumerations. AI incident management is a new compliance domain — traditional IT incident management processes must be extended to capture AI-specific event types.", "relevant_standards": [ "EU AI Act Article 61 — Post-market monitoring by providers of high-risk AI", "EU AI Act Article 62 — Reporting of serious incidents by providers and deployers", "EU AI Act Recital 99 — Definition of serious incident", "NIST AI RMF MANAGE 4.1 — Incident response for AI systems", "NIST AI 600-1 — GenAI risks including hallucination, data poisoning, prompt injection", "ISO/IEC 42001 Clause 9.1 — Monitoring and measurement of AI system performance", "DORA Article 17 — ICT-related incident management" ], "categories": [ { "enum_name": "AIIncidentSeverity", "label": "AI Incident Severity", "otel_attribute": "grc.ai_incident.severity", "opa_policy_path": "data.grc.ai_incident.severity", "rego_input_key": "grc_ai_incident_severity", "stability": "stable", "description": "Severity classification of an AI system incident. EU AI Act Article 62 requires providers of high-risk AI to report serious incidents to market surveillance authorities without undue delay. Severity classification drives regulatory reporting timelines and HITL escalation.", "permitted_values": [ "serious_incident_eu_ai_act_art62", "significant_incident_internal", "minor_incident", "near_miss", "anomaly_no_incident", "under_assessment" ], "value_labels": { "serious_incident_eu_ai_act_art62": "Serious Incident EU AI Act Art62", "significant_incident_internal": "Significant Incident Internal", "minor_incident": "Minor Incident", "near_miss": "Near Miss", "anomaly_no_incident": "Anomaly No Incident", "under_assessment": "Under Assessment" }, "code_definitions": { "serious_incident_eu_ai_act_art62": "EU AI Act Article 62 / Recital 99: incident that directly or indirectly leads to death, serious harm to health, serious harm to fundamental rights, serious damage to property or environment, or interruption of essential services. Providers must notify market surveillance authority without undue delay.", "significant_incident_internal": "Incident causing material operational impact, financial loss, or reputational harm but not meeting EU AI Act serious incident threshold; requires internal investigation and CIRO/CISO notification", "near_miss": "Event that could have caused a serious or significant incident but did not; must be investigated and logged; near-miss data feeds the AI risk model improvement cycle" }, "regulatory_mappings": { "eu_ai_act_art62": "EU AI Act Article 62 — Providers must report 'serious_incident_eu_ai_act_art62' to national market surveillance authority without undue delay; 15-day timeline for most cases; 2 days for life-threatening situations", "dora_art17": "DORA Article 17 — ICT-related incident classification; 'significant_incident_internal' with operational impact on financial entity may also trigger DORA major incident reporting", "nist_ai_rmf_manage_4_1": "NIST AI RMF MANAGE 4.1 — AI incident response plans must address all severity levels" }, "use_case": "AI incident management agent classifies all reported AI system events. 'Serious_incident_eu_ai_act_art62' classification triggers immediate CIRO + Legal notification and starts the EU AI Act reporting timeline. OPA policy blocks AI from autonomously classifying a 'serious_incident_eu_ai_act_art62' as a lower severity — downgrade requires human CCO approval.", "source": "EU AI Act Articles 61–62 and Recital 99; DORA Article 17; NIST AI RMF MANAGE 4.1", "source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689" }, { "enum_name": "AIIncidentRootCauseCategory", "label": "AI Incident Root Cause Category", "otel_attribute": "grc.ai_incident.root_cause_category", "opa_policy_path": "data.grc.ai_incident.root_cause_category", "rego_input_key": "grc_ai_incident_root_cause_category", "stability": "proposed", "description": "Root cause category for an AI system incident, aligned to NIST AI 600-1 GenAI risk taxonomy and OWASP Agentic Top 10 2026. Used to feed the AI risk model improvement cycle and regulatory post-market monitoring reports.", "permitted_values": [ "hallucination_factual_error", "prompt_injection_attack", "data_poisoning", "model_drift_performance_degradation", "training_data_bias", "out_of_distribution_input", "adversarial_input_manipulation", "tool_call_unauthorised_action", "memory_context_confusion", "multi_agent_orchestration_failure", "human_oversight_bypassed", "over_reliance_by_user", "misuse_by_deployer", "supply_chain_model_compromise", "infrastructure_failure_underlying" ], "value_labels": { "hallucination_factual_error": "Hallucination Factual Error", "prompt_injection_attack": "Prompt Injection Attack", "data_poisoning": "Data Poisoning", "model_drift_performance_degradation": "Model Drift Performance Degradation", "training_data_bias": "Training Data Bias", "out_of_distribution_input": "Out of Distribution Input", "adversarial_input_manipulation": "Adversarial Input Manipulation", "tool_call_unauthorised_action": "Tool Call Unauthorised Action", "memory_context_confusion": "Memory Context Confusion", "multi_agent_orchestration_failure": "Multi Agent Orchestration Failure", "human_oversight_bypassed": "Human Oversight Bypassed", "over_reliance_by_user": "Over Reliance by User", "misuse_by_deployer": "Misuse by Deployer", "supply_chain_model_compromise": "Supply Chain Model Compromise", "infrastructure_failure_underlying": "Infrastructure Failure Underlying" }, "regulatory_mappings": { "nist_ai_600_1": "NIST AI 600-1 GenAI Profile — Covers hallucination, CBRN uplift, data privacy, confabulation, prompt injection, and data poisoning risk categories", "owasp_agentic_top10_2026": "OWASP Agentic Top 10 2026 — Covers prompt injection, unauthorised tool use, memory poisoning, multi-agent trust failures, and excessive autonomy", "eu_ai_act_art62": "EU AI Act Article 62 — Root cause analysis must accompany serious incident report to market surveillance authority" }, "source": "NIST AI 600-1 GenAI Risk Taxonomy; OWASP Agentic Top 10 2026; EU AI Act recitals on serious incident investigation", "source_url": "https://airc.nist.gov/Docs/1" }, { "enum_name": "PostMarketMonitoringStatus", "label": "Post Market Monitoring Status", "otel_attribute": "grc.ai_post_market.monitoring_status", "opa_policy_path": "data.grc.ai_post_market.monitoring_status", "rego_input_key": "grc_ai_post_market_monitoring_status", "stability": "stable", "description": "Post-market monitoring lifecycle status for a high-risk AI system per EU AI Act Article 61. Providers must proactively collect and review post-deployment performance data for high-risk AI throughout the system's operational lifetime.", "permitted_values": [ "monitoring_plan_not_established", "monitoring_plan_approved", "actively_monitored", "review_triggered_by_incident", "review_triggered_by_performance_threshold", "corrective_action_in_progress", "monitoring_suspended_system_retired" ], "value_labels": { "monitoring_plan_not_established": "Monitoring Plan not Established", "monitoring_plan_approved": "Monitoring Plan Approved", "actively_monitored": "Actively Monitored", "review_triggered_by_incident": "Review Triggered by Incident", "review_triggered_by_performance_threshold": "Review Triggered by Performance Threshold", "corrective_action_in_progress": "Corrective Action in Progress", "monitoring_suspended_system_retired": "Monitoring Suspended System Retired" }, "regulatory_mappings": { "eu_ai_act_art61": "EU AI Act Article 61 — Post-market monitoring system required for all high-risk AI providers; must include data collection plan and performance review triggers", "eu_ai_act_art26_5": "EU AI Act Article 26(5) — Deployers of high-risk AI must monitor performance and report serious incidents to the provider; provider feeds this into their Article 61 monitoring system" }, "use_case": "AI GRC agent checks post-market monitoring status before processing any production AI deployment request. OPA policy blocks deployment of high-risk AI where monitoring_plan_not_established. 'Review_triggered_by_incident' status suspends new feature deployments pending review completion.", "source": "EU AI Act Article 61; EU AI Act Article 26(5)", "source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689" } ] }, { "subdomain": "ESG Programme Governance", "description": "Covers AI-driven ESG programme management enumerations including ESG framework alignment, double materiality process governance, and ESG assurance lifecycle. This subdomain covers the programme governance layer — ESG disclosure categories and data quality are in the Legal & RegTech vertical (file 10).", "relevant_standards": [ "ISSB IFRS S1/S2 (2023) — Sustainability-related financial disclosures", "EU CSRD (2022/2464) / ESRS — Corporate sustainability reporting", "GRI Standards (2021) — Global Reporting Initiative", "UN SDGs — Sustainable Development Goals framework", "SBTi Corporate Net-Zero Standard v1.1", "CDP (Carbon Disclosure Project) — Climate disclosure platform", "SASB Standards (now under ISSB)", "TCFD Recommendations (foundational, now integrated into ISSB S2)" ], "categories": [ { "enum_name": "ESGFrameworkAlignment", "label": "ESG Framework Alignment", "otel_attribute": "grc.esg.framework_alignment", "opa_policy_path": "data.grc.esg.framework_alignment", "rego_input_key": "grc_esg_framework_alignment", "stability": "stable", "description": "ESG reporting framework(s) to which an organisation's ESG programme is aligned. AI ESG programme management agents use this to correctly map data collection requirements and validate disclosure completeness.", "permitted_values": [ "issb_ifrs_s1_s2", "eu_csrd_esrs_mandatory", "eu_csrd_esrs_voluntary_sme", "gri_standards", "tcfd", "sasb", "un_sdgs", "science_based_targets_sbti", "cdp_climate", "cdp_water", "cdp_forests", "un_global_compact", "b_corp_assessment", "custom_stakeholder_framework" ], "value_labels": { "issb_ifrs_s1_s2": "ISSB IFRS S1 S2", "eu_csrd_esrs_mandatory": "EU CSRD ESRS Mandatory", "eu_csrd_esrs_voluntary_sme": "EU CSRD ESRS Voluntary Sme", "gri_standards": "Gri Standards", "tcfd": "Tcfd", "sasb": "Sasb", "un_sdgs": "Un Sdgs", "science_based_targets_sbti": "Science Based Targets Sbti", "cdp_climate": "Cdp Climate", "cdp_water": "Cdp Water", "cdp_forests": "Cdp Forests", "un_global_compact": "Un Global Compact", "b_corp_assessment": "B Corp Assessment", "custom_stakeholder_framework": "Custom Stakeholder Framework" }, "regulatory_mappings": { "eu_csrd": "EU CSRD — 'eu_csrd_esrs_mandatory' applies to large EU companies (FY2024 reporting) and listed SMEs (FY2026); mandatory third-party assurance required", "issb_ifrs_s1_s2": "ISSB — Voluntary internationally but adopted as mandatory in Australia, Canada, UK, Singapore, and others by 2026", "sec_climate_rule": "SEC Climate Rule (2024) — 'tcfd' alignment was the SEC's starting point; now superseded by SEC-specific requirements" }, "use_case": "AI ESG programme agent maps data collection requirements by framework. Multiple frameworks are common — a large EU company may be simultaneously subject to CSRD ESRS, ISSB S1/S2 (for IFRS reporters), CDP, and SBTi. AI identifies overlapping requirements to avoid duplicate data collection.", "source": "ISSB IFRS S1/S2; EU CSRD ESRS; GRI Standards", "source_url": "https://www.ifrs.org/issued-standards/ifrs-sustainability-standards-navigator/" }, { "enum_name": "ESGAssuranceLevel", "label": "ESG Assurance Level", "otel_attribute": "grc.esg.assurance_level", "opa_policy_path": "data.grc.esg.assurance_level", "rego_input_key": "grc_esg_assurance_level", "stability": "stable", "description": "Level of external third-party assurance obtained over ESG disclosures. EU CSRD mandates limited assurance initially, progressing to reasonable assurance. AI assurance workflow agents track assurance scope and level against regulatory requirements.", "permitted_values": [ "no_external_assurance", "management_assertion_only", "agreed_upon_procedures", "limited_assurance_isae3000", "reasonable_assurance_isae3000", "combined_audit_financial_esg", "third_party_verification_non_audit" ], "value_labels": { "no_external_assurance": "No External Assurance", "management_assertion_only": "Management Assertion Only", "agreed_upon_procedures": "Agreed Upon Procedures", "limited_assurance_isae3000": "Limited Assurance Isae 3000", "reasonable_assurance_isae3000": "Reasonable Assurance Isae 3000", "combined_audit_financial_esg": "Combined Audit Financial ESG", "third_party_verification_non_audit": "Third Party Verification Non Audit" }, "code_definitions": { "limited_assurance_isae3000": "ISAE 3000 limited assurance: practitioner's conclusion expressed in negative form ('nothing has come to our attention'); EU CSRD mandatory from first reporting year (FY2024 for large companies)", "reasonable_assurance_isae3000": "ISAE 3000 reasonable assurance: practitioner's conclusion expressed in positive form; higher evidence requirements; EU CSRD target from 2028 onwards (subject to Commission assessment)", "combined_audit_financial_esg": "Statutory auditor provides combined assurance over financial statements and sustainability report in a single engagement; emerging practice under EU CSRD" }, "ordered": true, "value_ordinals": { "no_external_assurance": 1, "management_assertion_only": 2, "agreed_upon_procedures": 3, "limited_assurance_isae3000": 4, "reasonable_assurance_isae3000": 5, "combined_audit_financial_esg": 6, "third_party_verification_non_audit": 7 }, "regulatory_mappings": { "eu_csrd_art34": "EU CSRD Article 34 — Limited assurance required for sustainability statements; progression to reasonable assurance subject to Commission review by 2028", "issb_ifrs_s1": "ISSB IFRS S1 — Assurance requirements determined by applicable jurisdiction regulation; many jurisdictions moving to limited assurance requirements" }, "source": "EU CSRD Article 34; ISAE 3000 (Revised); IAASB Sustainability Assurance Standards (ISSA 5000, approved 2024)", "source_url": "https://www.iaasb.org/focus-areas/sustainability-reporting" } ] }, { "subdomain": "Internal Audit & Assurance for AI", "description": "Covers IIA Standards, AI audit methodology, and AI assurance programme enumerations. Internal audit functions are increasingly being asked to provide assurance over AI systems — this subdomain operationalises that capability.", "relevant_standards": [ "IIA International Standards for the Professional Practice of Internal Auditing (2024 edition)", "IIA Global Technology Audit Guide (GTAG) — Auditing Artificial Intelligence (2023)", "IIA Three Lines Model (2020)", "ISAE 3000 (Revised) — Assurance Engagements Other than Audits", "PCAOB AS 2201 — Integrated Audit of ICFR (relevant where AI is in financial reporting controls)", "SOC 2 (AICPA TSP 100) — Trust Service Criteria for AI service organisations" ], "categories": [ { "enum_name": "AIAuditEngagementType", "label": "AI Audit Engagement Type", "otel_attribute": "grc.internal_audit.ai_engagement_type", "opa_policy_path": "data.grc.internal_audit.ai_engagement_type", "rego_input_key": "grc_internal_audit_ai_engagement_type", "stability": "stable", "description": "Type of internal audit engagement covering AI systems. IIA GTAG on Auditing AI identifies multiple engagement types. AI GRC agents generate audit universe entries and schedule engagements by type based on risk tiering.", "permitted_values": [ "ai_governance_programme_audit", "individual_ai_system_audit", "model_risk_management_audit", "ai_bias_fairness_audit", "ai_data_governance_audit", "ai_security_audit", "ai_third_party_vendor_audit", "ai_regulatory_compliance_audit", "ai_operational_resilience_audit", "ai_ethics_review", "continuous_ai_monitoring" ], "value_labels": { "ai_governance_programme_audit": "AI Governance Programme Audit", "individual_ai_system_audit": "Individual AI System Audit", "model_risk_management_audit": "Model Risk Management Audit", "ai_bias_fairness_audit": "AI Bias Fairness Audit", "ai_data_governance_audit": "AI Data Governance Audit", "ai_security_audit": "AI Security Audit", "ai_third_party_vendor_audit": "AI Third Party Vendor Audit", "ai_regulatory_compliance_audit": "AI Regulatory Compliance Audit", "ai_operational_resilience_audit": "AI Operational Resilience Audit", "ai_ethics_review": "AI Ethics Review", "continuous_ai_monitoring": "Continuous AI Monitoring" }, "use_case": "AI audit planning agent builds the annual AI audit plan by mapping each deployed AI system to the appropriate engagement type based on risk tier, regulatory requirements, and prior findings. Tier A critical AI systems require at minimum 'individual_ai_system_audit' and 'ai_security_audit' annually.", "source": "IIA GTAG — Auditing Artificial Intelligence (2023); IIA International Standards 2024 edition", "source_url": "https://www.theiia.org/en/standards/" }, { "enum_name": "AuditFindingSeverity", "label": "Audit Finding Severity", "otel_attribute": "grc.internal_audit.finding_severity", "opa_policy_path": "data.grc.internal_audit.finding_severity", "rego_input_key": "grc_internal_audit_finding_severity", "stability": "stable", "description": "Severity classification of an internal audit finding related to an AI system. Finding severity drives escalation, remediation timeline, and Board Audit Committee reporting.", "permitted_values": [ "critical", "high", "medium", "low", "informational", "best_practice_recommendation" ], "value_labels": { "critical": "Critical", "high": "High", "medium": "Medium", "low": "Low", "informational": "Informational", "best_practice_recommendation": "Best Practice Recommendation" }, "code_definitions": { "critical": "Immediate action required; material control failure; regulatory breach risk or financial harm exposure; escalation to Board Audit Committee and C-suite mandatory; AI system may need to be suspended pending remediation", "high": "Significant control weakness; elevated risk of harm, compliance breach, or financial loss; management response required within 30 days; escalation to CEO/CRO", "medium": "Control weakness that should be addressed; moderate risk of impact; management response required within 90 days", "low": "Minor control gap; limited impact risk; management response within 180 days", "informational": "Observation with no immediate risk impact; no management response required; noted for awareness" }, "ordered": true, "value_ordinals": { "critical": 1, "high": 2, "medium": 3, "low": 4, "informational": 5, "best_practice_recommendation": 6 }, "use_case": "AI audit findings management agent classifies audit findings. OPA policy enforces that 'critical' audit findings on AI systems trigger AI system suspension assessment and Board Audit Committee notification within 24 hours. AI cannot autonomously clear a 'critical' finding — only the CAE or CFO can close critical findings.", "source": "IIA International Standards 2024; common internal audit finding severity taxonomies (IIA, ISACA, ACFE)", "source_url": "https://www.theiia.org/en/standards/" } ] }, { "subdomain": "Board & Executive AI Governance", "description": "Covers board-level AI governance obligations, Chief AI Officer enablement, and enterprise AI programme governance enumerations. These enums support the board reporting and C-suite accountability layer of the AI GRC portal.", "relevant_standards": [ "EU AI Act Article 17 — Quality management system; board accountability for high-risk AI providers", "OMB M-24-10 Section 4 — Chief AI Officer designation and responsibilities", "EO 14110 Section 10 — Agency AI governance and accountability", "SEC Cybersecurity Disclosure Rule (2023) — Board cybersecurity expertise disclosure (analogous for AI)", "NACD Director's Handbook on Artificial Intelligence (2024)", "WEF AI Governance Alliance — Board AI governance principles (2024)", "IIA Three Lines Model — Board, management, and assurance roles" ], "categories": [ { "enum_name": "AIGovernanceBodyType", "label": "AI Governance Body Type", "otel_attribute": "grc.ai_governance.governance_body_type", "opa_policy_path": "data.grc.ai_governance.governance_body_type", "rego_input_key": "grc_ai_governance_governance_body_type", "stability": "proposed", "description": "Type of AI governance body responsible for oversight of AI systems and AI risk management. Multiple governance bodies may exist at different organisational levels. AI GRC agents route escalations and approvals to the correct body based on the decision type.", "permitted_values": [ "board_of_directors", "board_audit_committee", "board_risk_committee", "board_technology_committee", "executive_ai_steering_committee", "chief_ai_officer", "chief_risk_officer", "ai_review_board", "model_risk_committee", "data_ethics_committee", "operational_ai_governance_team", "business_unit_ai_owner" ], "value_labels": { "board_of_directors": "Board of Directors", "board_audit_committee": "Board Audit Committee", "board_risk_committee": "Board Risk Committee", "board_technology_committee": "Board Technology Committee", "executive_ai_steering_committee": "Executive AI Steering Committee", "chief_ai_officer": "Chief AI Officer", "chief_risk_officer": "Chief Risk Officer", "ai_review_board": "AI Review Board", "model_risk_committee": "Model Risk Committee", "data_ethics_committee": "Data Ethics Committee", "operational_ai_governance_team": "Operational AI Governance Team", "business_unit_ai_owner": "Business Unit AI Owner" }, "use_case": "AI GRC escalation routing engine maps each decision type and risk tier to the correct governance body. 'Exceeds_tolerance' risk events route to Board Risk Committee. 'Critical' audit findings route to Board Audit Committee. New high-risk AI deployments route to AI Review Board or Executive AI Steering Committee for approval.", "source": "NACD AI Director's Handbook (2024); WEF AI Governance Alliance; IIA Three Lines Model; EU AI Act Article 17 quality management responsibilities", "source_url": "https://www.nacdonline.org/" }, { "enum_name": "AIBoardReportingFrequency", "label": "AI Board Reporting Frequency", "otel_attribute": "grc.ai_governance.board_reporting_frequency", "opa_policy_path": "data.grc.ai_governance.board_reporting_frequency", "rego_input_key": "grc_ai_governance_board_reporting_frequency", "stability": "proposed", "description": "Frequency at which AI risk, performance, and governance metrics are reported to the board or board committee. Drives the AI GRC portal dashboard generation and distribution schedule.", "permitted_values": [ "real_time_dashboard", "weekly", "monthly", "quarterly", "semi_annually", "annually", "event_driven_threshold_breach", "ad_hoc_on_request" ], "value_labels": { "real_time_dashboard": "Real-Time Dashboard", "weekly": "Weekly", "monthly": "Monthly", "quarterly": "Quarterly", "semi_annually": "Semi-Annually", "annually": "Annually", "event_driven_threshold_breach": "Event-Driven Threshold Breach", "ad_hoc_on_request": "Ad Hoc on Request" }, "use_case": "AI GRC portal generates board-ready AI risk dashboards at the configured reporting frequency. 'Event_driven_threshold_breach' triggers an out-of-cycle board notification when a risk appetite breach, serious incident, or critical audit finding occurs regardless of the normal reporting cadence.", "source": "NACD AI Director's Handbook; SEC disclosure obligations; IIA board reporting guidance", "source_url": "https://www.nacdonline.org/" } ] } ], "opa_rego_policy_patterns": { "description": "GRC, ESG & Enterprise Risk Management-specific OPA Rego policy patterns referencing enum values from this file and from 00_core_sdk_and_governance.json. Illustrative patterns, not production policies.", "patterns": [ { "pattern_id": "grc.block_high_risk_ai_deployment_without_impact_assessment", "pattern_name": "block_high_risk_ai_deployment_without_impact_assessment", "enforcement_effect": "deny", "description": "Block production deployment of any AI system classified as high-risk or above where the AI impact assessment has not been completed with an acceptable outcome. Implements ISO/IEC 42001 Annex A.5, EU AI Act Article 9, and the enterprise AI deployment governance gate in a single policy.", "applicable_enums": [ "AIImpactAssessmentOutcome", "AIGovernanceMaturityLevel", "RiskAppetiteStatus", "NISTAIRMFImplementationTier" ], "regulatory_basis": "ISO/IEC 42001:2023 Annex A.5 — AI system impact assessment is a normative control required before deployment; EU AI Act Article 9(4) — Testing and evaluation must be performed for high-risk AI; NIST AI RMF MAP 2.2 — Impact assessment before deployment", "rego_sketch": "package grc.ai_deployment_governance\n\nblocking_assessment_outcomes := {\n \"assessment_not_conducted\",\n \"assessment_in_progress\",\n \"unacceptable_impact_deployment_blocked\"\n}\n\nhigh_risk_maturity_required := \"level_3_managed\"\n\ndeny[msg] {\n input.grc_ai_impact_assessment_outcome in blocking_assessment_outcomes\n msg := sprintf(\"ISO 42001 Annex A.5 / EU AI Act Art 9: AI system '%v' cannot be deployed — impact assessment outcome is '%v'. A completed assessment with acceptable outcome is required.\", [input.ai_system_id, input.grc_ai_impact_assessment_outcome])\n}\n\ndeny[msg] {\n input.grc_ai_impact_assessment_outcome == \"critical_impact_board_approval_required\"\n not input.board_risk_committee_approval_on_file == true\n msg := sprintf(\"GRC Governance: AI system '%v' has critical impact assessment outcome. Board Risk Committee approval required before deployment.\", [input.ai_system_id])\n}\n\ndeny[msg] {\n input.ai_system_risk_tier in {\"tier_4_critical\", \"tier_3_high\"}\n input.grc_ai_governance_maturity_level in {\"level_0_ad_hoc\", \"level_1_aware\"}\n msg := sprintf(\"AI Governance Maturity: Deploying AI at risk tier '%v' requires at least '%v' governance maturity. Current maturity is '%v'.\", [input.ai_system_risk_tier, high_risk_maturity_required, input.grc_ai_governance_maturity_level])\n}" }, { "pattern_id": "grc.enforce_eu_ai_act_serious_incident_reporting_gate", "pattern_name": "enforce_eu_ai_act_serious_incident_reporting_gate", "enforcement_effect": "deny", "description": "Block any AI incident management agent from downgrading a 'serious_incident_eu_ai_act_art62' classification to a lower severity without CCO approval, and enforce that regulatory notification is initiated within the EU AI Act prescribed timeline. Prevents AI-driven incident suppression.", "applicable_enums": [ "AIIncidentSeverity", "AIIncidentRootCauseCategory", "PostMarketMonitoringStatus", "ComplianceActionStatus" ], "regulatory_basis": "EU AI Act Article 62 — Providers must report serious incidents without undue delay; life-threatening situations within 2 days; all other serious incidents within 15 days; providers cannot suppress or delay reporting", "rego_sketch": "package grc.ai_incident_management\n\ndeny[msg] {\n input.previous_incident_severity == \"serious_incident_eu_ai_act_art62\"\n input.proposed_incident_severity != \"serious_incident_eu_ai_act_art62\"\n not input.cco_hitl_approved_downgrade == true\n msg := \"EU AI Act Art 62: Downgrading a serious incident classification requires CCO approval. AI incident management agent cannot autonomously reclassify serious incidents to lower severity.\"\n}\n\ndeny[msg] {\n input.grc_ai_incident_severity == \"serious_incident_eu_ai_act_art62\"\n not input.regulatory_notification_initiated == true\n input.hours_since_incident_detected > 14\n msg := sprintf(\"EU AI Act Art 62: Serious incident '%v' detected %v hours ago. Regulatory notification must be submitted within 15 days. CIRO and Legal must initiate notification immediately.\", [input.incident_id, input.hours_since_incident_detected])\n}\n\ndeny[msg] {\n input.grc_ai_incident_severity == \"serious_incident_eu_ai_act_art62\"\n input.involves_life_threatening_harm == true\n not input.regulatory_notification_initiated == true\n input.hours_since_incident_detected > 1\n msg := \"EU AI Act Art 62: Life-threatening serious incident requires regulatory notification within 2 days. IMMEDIATE action required.\"\n}" }, { "pattern_id": "grc.enforce_model_validation_gate_sr117", "pattern_name": "enforce_model_validation_gate_sr117", "enforcement_effect": "deny", "description": "Block deployment of any AI model where SR 11-7 / ISO 42001 model validation has not been completed, is overdue, or has failed. Also enforces that models undergoing material-change-triggered revalidation receive enhanced runtime monitoring flags.", "applicable_enums": [ "ModelValidationStatus", "ThirdPartyAIRiskTier", "AIGovernanceMaturityLevel", "NISTAIRMFImplementationTier" ], "regulatory_basis": "Federal Reserve SR 11-7 — All material models must be validated before use and on a periodic basis; OCC 2011-12; EU AI Act Article 9(5) — Risk management system must include model testing; ISO/IEC 42001 Clause 8 — Operational control of AI systems", "rego_sketch": "package grc.model_risk_management\n\nblocking_validation_statuses := {\n \"pre_validation_development\",\n \"initial_validation_failed_blocked\",\n \"validation_overdue\"\n}\n\ndeny[msg] {\n input.grc_model_validation_status in blocking_validation_statuses\n msg := sprintf(\"SR 11-7 / ISO 42001: Model '%v' cannot be deployed — validation status is '%v'. Completed validation with approved outcome is required before production use.\", [input.model_id, input.grc_model_validation_status])\n}\n\ndeny[msg] {\n input.grc_third_party_ai_risk_tier == \"not_assessed\"\n msg := sprintf(\"Third-Party AI Risk: Vendor '%v' has not been assessed. Production use of unassessed AI vendors is prohibited. Complete tier classification before deployment.\", [input.vendor_id])\n}\n\nwarn[msg] {\n input.grc_model_validation_status == \"material_change_triggered_revalidation\"\n msg := sprintf(\"SR 11-7: Model '%v' is undergoing revalidation triggered by material change. Enhanced runtime monitoring is required until revalidation is complete.\", [input.model_id])\n}" }, { "pattern_id": "grc.enforce_risk_appetite_escalation", "pattern_name": "enforce_risk_appetite_escalation", "enforcement_effect": "deny", "description": "Block AI risk management agents from applying 'accept_retain' treatment to any risk classified as exceeding the board-approved tolerance threshold, and enforce automatic escalation to the correct governance body based on the risk appetite status.", "applicable_enums": [ "RiskAppetiteStatus", "RiskTreatmentStrategy", "AIGovernanceBodyType", "ComplianceActionStatus" ], "regulatory_basis": "COSO ERM 2017 Principle 6 — Board defines risk oversight and appetite; ISO 31000:2018 Section 6.5 — Risk treatment requires documented authorisation; EU AI Act Article 9(3) — Residual risks must be acceptable before market release", "rego_sketch": "package grc.risk_management\n\ndeny[msg] {\n input.grc_risk_appetite_status == \"exceeds_tolerance\"\n input.grc_risk_treatment_strategy == \"accept_retain\"\n not input.risk_committee_hitl_approval == true\n msg := sprintf(\"COSO ERM / ISO 31000: Risk '%v' exceeds tolerance threshold. 'accept_retain' treatment requires explicit Risk Committee or CRO approval. AI cannot autonomously accept above-tolerance risks.\", [input.risk_id])\n}\n\ndeny[msg] {\n input.grc_risk_appetite_status in {\"exceeds_tolerance\", \"within_tolerance_above_appetite\"}\n not input.escalation_initiated == true\n msg := sprintf(\"Risk Appetite Breach: Risk '%v' at status '%v' requires formal escalation to the appropriate governance body. Escalation must be initiated before any remediation or acceptance decisions.\", [input.risk_id, input.grc_risk_appetite_status])\n}" }, { "pattern_id": "grc.enforce_iso42001_deployment_clause8_gate", "pattern_name": "enforce_iso42001_deployment_clause8_gate", "enforcement_effect": "deny", "description": "Block production deployment of any new AI system where mandatory ISO/IEC 42001 Clause 8 operational controls — specifically Annex A.5 (AI system impact assessment), Annex A.6 (AI system lifecycle planning), and Annex A.7 (data governance) — have not been completed and evidenced in the GRC platform.", "applicable_enums": [ "ISO42001ClauseArea", "AIImpactAssessmentOutcome", "ModelValidationStatus", "AIGovernanceMaturityLevel" ], "regulatory_basis": "ISO/IEC 42001:2023 Clause 8 — Operational planning and control; Annex A.5 — AI system impact assessment (normative control); Annex A.6 — AI system lifecycle; Annex A.7 — Data for AI systems; EU AI Act Article 17 — Quality management system requirements", "rego_sketch": "package grc.iso42001_compliance\n\nmandatory_clause8_controls := {\n \"annex_a_controls\",\n \"clause_8_operation\"\n}\n\nrequired_annex_a_controls := {\n \"A.5_impact_assessment\",\n \"A.6_lifecycle_planning\",\n \"A.7_data_governance\"\n}\n\ndeny[msg] {\n some control in required_annex_a_controls\n not input.iso42001_controls_completed[control] == true\n msg := sprintf(\"ISO/IEC 42001 Annex A: Control '%v' must be completed before AI system '%v' can be deployed. Evidence must be available in the GRC platform.\", [control, input.ai_system_id])\n}\n\ndeny[msg] {\n input.grc_ai_impact_assessment_outcome == \"assessment_not_conducted\"\n msg := sprintf(\"ISO/IEC 42001 Annex A.5: AI system impact assessment has not been conducted for system '%v'. This is a normative control — deployment is blocked until assessment is completed with an acceptable outcome.\", [input.ai_system_id])\n}" } ] }, "agent_registry_fields": { "description": "Recommended fields for registering a GRC, ESG, or enterprise risk management domain agentic AI system in the GRC portal. Supplements the core agent identity schema from 00_core_sdk_and_governance.json.", "fields": [ { "field": "iso42001_certification_status", "type": "string", "description": "ISO/IEC 42001:2023 certification status of the organisation or business unit responsible for this AI system. Certified organisations have demonstrated conformity with the AI management system standard through third-party audit. Use values such as not_certified, readiness_assessment_in_progress, stage_1_audit_completed, stage_2_audit_in_progress, certified, certified_surveillance_due, or certification_expired.", "required_when": "All AI agents operated by organisations pursuing or maintaining ISO/IEC 42001 certification" }, { "field": "nist_ai_rmf_implementation_tier", "type": "enum", "enum_ref": "NISTAIRMFImplementationTier", "description": "Organisation's current NIST AI RMF implementation tier for the function most relevant to this AI agent's role (GOVERN for governance agents, MANAGE for operational agents). Drives the depth of GRC programme oversight applied.", "required_when": "All AI agents deployed in organisations with a formal NIST AI RMF-aligned governance programme" }, { "field": "eu_ai_act_provider_role", "type": "string", "description": "The organisation's role in the EU AI Act supply chain for this AI system. Role determines which obligations apply under Title III. Use values such as provider, deployer, importer, distributor, product_manufacturer_integrating_ai, or not_eu_regulated.", "required_when": "All AI systems potentially subject to EU AI Act obligations" }, { "field": "eu_ai_act_art62_reporting_active", "type": "boolean", "description": "True if this AI system is a high-risk AI system subject to EU AI Act Article 62 serious incident reporting obligations. Activates the incident severity classification and regulatory notification workflow for this agent.", "required_when": "All AI systems classified as high-risk under EU AI Act Annex III or Article 6" }, { "field": "sr117_model_inventory_id", "type": "string", "description": "Model inventory identifier in the SR 11-7 / OCC 2011-12 model risk management system. Links AI agent operational telemetry to the model validation record and model risk rating for financial services entities.", "required_when": "All AI agents deployed at Federal Reserve-supervised, OCC-supervised, or equivalent financial institution" }, { "field": "dora_ict_register_entry", "type": "boolean", "description": "True if this AI agent or its underlying AI service provider is registered in the DORA Article 28 ICT third-party service provider register. Required for EU financial entities subject to DORA.", "required_when": "All AI agents deployed at EU financial entities subject to DORA, where the underlying AI platform is an ICT service provider" }, { "field": "post_market_monitoring_plan_id", "type": "string", "description": "Reference to the EU AI Act Article 61 post-market monitoring plan for this high-risk AI system. The plan defines data collection triggers, performance review thresholds, and corrective action criteria.", "required_when": "All high-risk AI systems provided by an EU AI Act Article 61-obligated provider" }, { "field": "risk_appetite_framework_version", "type": "string", "description": "Version identifier of the board-approved enterprise risk appetite framework against which this AI agent's risk exposures are evaluated. Ensures AI risk assessments use the current, board-approved appetite thresholds.", "required_when": "All AI agents whose outputs feed the enterprise risk register or board risk reporting" } ] } }