{ "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": "Governance, risk, and compliance fields describe risk assessments, controls, vendor reviews, audits, incidents, and management reporting. ESG fields support recorded sustainability workflows.", "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, 72, 73", "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 72, Post-market monitoring by providers", "EU AI Act Article 73, 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)", "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": "Connect each reported result to its scope, owner, and evidence. Keep risk estimates, measured outcomes, and unresolved findings distinct in executive reports.", "key_regulatory_risk_note": "Framework mapping is not certification. Record which requirements were assessed and what evidence supports the conclusion; do not infer compliance from a vocabulary match." }, "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 that looks for vulnerabilities and unexpected model behavior. The assessment plan defines the team's independence and the scope of testing.", "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": "Article 9 requires assessment and management of risks for high-risk AI. The organization's risk policy assigns approval authority for accepting remaining risks.", "iso_31000_6_5": "ISO 31000:2018 Section 6.5, Risk treatment; treatment plan must be documented with risk owner, timeline, and monitoring arrangements" }, "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 73, 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": "Article 17 requires a documented quality management system for providers of high-risk AI. Review the required controls directly; a maturity tier does not establish compliance.", "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": "Articles 9 and 17 address risk and quality management systems. This maturity level records an organizational assessment, not a statutory minimum.", "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": "Fields for AI vendor due diligence and monitoring. Apply the requirements relevant to your institution and activity.", "relevant_standards": [ "OCC 2011-12, Model Risk Management", "EU AI Act Articles 23-26, Importer, distributor, provider and deployer responsibilities", "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": "The institution's risk tier for an AI vendor or model provider. Use it to determine due diligence and monitoring under your vendor governance policy.", "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": "A vendor whose failure or loss would cause material disruption or harm. The institution defines the evidence, review frequency, and exit planning required for this tier.", "tier_b_significant": "A vendor whose failure would cause significant but manageable disruption. Apply the institution's due-diligence and monitoring requirements for this tier.", "tier_c_moderate": "An AI component with limited operational dependency. Record the review requirements assigned under the institution's vendor policy.", "tier_d_low": "A tool assessed as having no sensitive-data access or operational dependency. Apply the institution's procurement requirements.", "not_assessed": "The vendor has been identified but not yet assessed. The institution's policy determines whether onboarding can proceed." }, "regulatory_mappings": { "eu_ai_act_art25": "Article 25 identifies when rebranding, substantial modification, or a changed intended purpose makes another party a provider of high-risk AI.", "dora_art28": "Article 28 addresses ICT third-party risk management. An institution's internal tier is separate from the European Supervisory Authorities' critical-provider designation under Article 31.", "nist_ai_rmf_govern_6_1": "NIST AI RMF GOVERN 6.1, Third-party AI risk management policies and practices" }, "use_case": "An institution's rule stops onboarding until the vendor assessment is recorded. Critical dependencies follow the review schedule and approval process set by its vendor policy.", "source": "Institution-defined vendor tiers; DORA Articles 28 and 31; EU AI Act Article 25; NIST AI RMF GOVERN 6.1" }, { "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": "Validation status reported by the institution. For generative and agentic AI, the institution sets validation requirements under its own governance policy.", "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": "Validation has passed the institution's scheduled review date. Apply the escalation and monitoring steps defined by its policy." }, "regulatory_mappings": { "eu_ai_act_art9": "Article 9(6)-(8) addresses testing against defined metrics and thresholds. The organization's validation policy determines when a failed check blocks release." }, "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": "institution-defined AI validation lifecycle" }, { "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": "Record the evidence reviewed for a general-purpose AI model provider's obligations. Articles 53 and 55 distinguish general requirements from additional duties for models with systemic risk.", "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": "Systemic-risk model adversarial testing recorded", "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": "Evidence records the provider's adversarial testing under Article 55(1)(a). The testing must be documented; this status does not imply that all results are public.", "code_of_practice_signatory": "The provider has signed the General-Purpose AI Code of Practice. Record adherence evidence separately from the signature." }, "regulatory_mappings": { "eu_ai_act_art53": "Article 53 addresses technical documentation, information for downstream system providers, a copyright policy, and a public summary of training content, with specified exceptions.", "eu_ai_act_art55": "Article 55 adds model evaluation, systemic-risk assessment and mitigation, serious-incident reporting, and cybersecurity duties for providers of models with systemic risk.", "eu_ai_office_code": "The General-Purpose AI Code of Practice is a voluntary way to demonstrate compliance. Harmonised standards, rather than signing the Code alone, provide a presumption of conformity." }, "source": "EU AI Act Articles 53 and 55; General-Purpose AI Code of Practice", "source_url": "https://digital-strategy.ec.europa.eu/en/policies/contents-code-gpai" } ] }, { "subdomain": "AI Incident Management & Post-Market Monitoring", "description": "Covers EU AI Act Article 73 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 72, Post-market monitoring by providers of high-risk AI", "EU AI Act Article 73, 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 73 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 73 / 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 73 sets serious incident reporting duties. Determine the deadline from the incident type and circumstances; the classification label does not set it.", "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 73, 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 72. 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 72, 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 72 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 72; 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", "display_name": "Require an accepted impact assessment before deployment", "enforcement_effect": "deny", "description": "Hold deployment until the required impact assessment is accepted. Your organization sets which applications need assessment and what acceptance requires.", "applicable_enums": [ "AIImpactAssessmentOutcome", "AIGovernanceMaturityLevel", "RiskAppetiteStatus", "NISTAIRMFImplementationTier" ], "regulatory_basis": "Assessment-control references, where applicable: ISO/IEC 42001 Annex A.5, EU AI Act Article 9, and NIST AI RMF MAP." }, { "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": "Require authorized review before lowering a serious incident's severity. Track notification deadlines separately and record whether each notification was delivered. A classification rule alone does not complete regulatory reporting.", "applicable_enums": [ "AIIncidentSeverity", "AIIncidentRootCauseCategory", "PostMarketMonitoringStatus", "ComplianceActionStatus" ], "regulatory_basis": "EU AI Act Article 73 sets incident reporting duties and timelines. Determine the applicable deadline from the incident type and circumstances." }, { "pattern_id": "grc.require_institution_model_validation", "pattern_name": "require_institution_model_validation", "enforcement_effect": "deny", "description": "Example institution-defined control: block deployment when the model does not meet the institution's validation policy. Require the selected monitoring status after material changes.", "applicable_enums": [ "ModelValidationStatus", "ThirdPartyAIRiskTier", "AIGovernanceMaturityLevel", "NISTAIRMFImplementationTier" ], "regulatory_basis": "Institution-defined model validation policy." }, { "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" }, { "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" } ] }, "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": "Record the organization's role for this AI system to identify its EU AI Act obligations. Examples include provider, deployer, importer, and distributor.", "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 73 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": "model_inventory_id", "type": "string", "description": "The institution's model inventory identifier. Connects reported AI activity to its validation record and risk assessment.", "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 72 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 72-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" } ] } }