diff --git a/rag-agentic-dashboard/public/master-reference.html b/rag-agentic-dashboard/public/master-reference.html index 14f8032..a437b7c 100644 --- a/rag-agentic-dashboard/public/master-reference.html +++ b/rag-agentic-dashboard/public/master-reference.html @@ -5,54 +5,99 @@ MREF-GSIFI-WP-023 — Institutional-Grade AGI/ASI Governance Master Reference 2026-2030
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Institutional-Grade AGI/ASI Governance Master Reference 2026-2030

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MREF-GSIFI-WP-023 v1.0.0 | 2026-04-07 | CONFIDENTIAL — Board / C-Suite / Regulators | Fortune 500 · Global 2000 · G-SIFIs
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Institutional-Grade AGI/ASI Governance Master Reference 2026-2030LIVE

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MREF-GSIFI-WP-023 v1.0.0 | 2026-04-08 | CONFIDENTIAL — Board / C-Suite / Regulators / Enterprise Architecture | Fortune 500 · Global 2000 · 30 G-SIFIs
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+ Sentinel v2.4 Online + EAIP 10,400 RPC/s + Kafka 45K evt/s + Policy Engine 4.2ms P99 +
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Institutional-Grade AGI/ASI Governance Master Reference 2026-2030

Executive Summary

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Investment Summary

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Investment & ROI Summary

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Regulatory Compliance Scores by Framework
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Investment Allocation (5-Year)
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Strategic KPI Trajectory (2024-2030)

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Governance Maturity Progression
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Document Hierarchy

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Regulatory Compliance Architecture — 8 Framework Integration

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Regulatory Compliance Architecture — 8 Framework Integration Across 4 Jurisdictions

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Framework Details

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FrameworkJurisdictionOPA RulesSentinel RulesCompliance %Status
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Cross-Framework Overlap Matrix

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Framework Detail Matrix

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FrameworkJurisdictionOPA RulesSentinel RulesComplianceTrendStatus
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Cross-Framework Overlap & Coverage Matrix

Framework PairOverlapsUnique LeftUnique RightCoverage
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Gap Analysis

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Compliance Gap Analysis

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IDDescriptionFrameworksRemediationTimelineOwner
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IDSeverityDescriptionFrameworksRemediationTimelineEffortOwner
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Multilayered AI Governance Structure — 8 Pillars

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Multilayered AI Governance Architecture — 8 Pillars, 6 Layers

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Governance Pillars

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Decision Hierarchy

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LevelAuthorityCadenceEscalation Trigger
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AGI Incident Escalation Phases

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PhaseSLAActionsResponsible
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Severity Levels

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LevelNameDescriptionResponseExample
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Decision Hierarchy — 4 Authority Levels

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LevelAuthorityMembersCadenceEscalation TriggerSLA
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RACI Matrix — Key Governance Activities

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AGI Incident Escalation Framework — 6 Phases

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PhaseSLAActionsResponsibleNotification
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Severity Classification — 5 Levels

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LevelNameCriteriaResponse SLAAuthorityExample
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Technical Implementation — Reference Architecture & Trust Stack

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Enterprise AI Governance Architecture v3.0

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LayerComponentsTechnologyGovernance
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Technical Implementation — Enterprise AI Governance Architecture v3.0

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6-Layer Reference Architecture

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LayerComponentsTechnology StackGovernance Controls

Enterprise Trust & Compliance Stack v2.0

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LayerComponentsStandard
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Kafka ACL Governance — 12 Topics

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TopicPartitionsRetentionACL
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Terraform IaC — 8 Modules, 144 Resources

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LayerComponentsStandard Alignment
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Kafka ACL Governance — 12 Topics, 312 ACL Rules

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TopicPartitionsRetentionACL Controls
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WORM Evidence Storage

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Policy Engine Performance

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Terraform IaC Governance — 8 Modules, 144 Resources

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ModuleResourcesDescription
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GateToolBlocks On
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ModuleResourcesDescriptionDrift Check
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GateToolBlocks OnStatus
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Auditor Workflows

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ModeDescriptionSLATools
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Auditor Workflow Modes — Evidence Bundle Generation

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ModeDescriptionSLAEvidence TypesRegulator Standards

Financial Services AI Governance — G-SIFI Model Risk Management

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Model Inventory by Category

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CategoryModelsTierSR 11-7Key Metric
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SR 11-7 Validation Stages

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StageActivitiesOwnerTimelineArtifacts
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Fair Lending Controls

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EARL Maturity Model

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Model Inventory by Category — 847 Models (312 Production)

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CategoryModelsProductionRisk TierSR 11-7 SectionKey PerformanceLast Validated
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SR 11-7 Validation Pipeline — 6 Stages

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StageActivitiesOwnerTimelineArtifacts
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Fair Lending Compliance (FCRA/ECOA)

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EARL Maturity Assessment

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Frontier AGI Safety — Trust-by-Design & Containment

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Trust-by-Design Principles

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IDPrincipleImplementationStatus
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AGI Alignment Verification Protocol (AAVP)

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Frontier AGI Safety — Trust-by-Design, Alignment Verification & Containment

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Trust-by-Design Principles — 8 Core Principles

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IDPrincipleImplementation ApproachVerificationStatus
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AGI Alignment Verification Protocol (AAVP) — 2,847 Tests

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CategoryTestsThresholdScoreStatus
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Containment Architecture — 5 Layers

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LayerDescriptionControlsStatus
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AGI Readiness Levels (ARL)

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LevelNameDescriptionMilestone
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CategoryTestsThresholdCurrentStatus
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Alignment Score Distribution
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Containment Architecture — 5 Active Layers

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LayerDescriptionControlsLast TestStatus
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AGI Readiness Levels (ARL-1 through ARL-7)

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Global Governance — ICGC, Compute Registry & Cross-Border Coordination

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Global Governance — International Compute Governance & Cross-Border Coordination

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International Compute Governance Consortium (ICGC) — 15 Components

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IDNameFunctionStatusTimeline
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Global Compute Registry

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CategoryCountThresholdRequirement
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IDNameFunctionMembersStatusTimeline
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Global Compute Registry (GCR)

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CategoryCountThresholdRequirementCompliance

Cross-Border Coordination Mechanisms

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MechanismDescriptionStatusParticipants
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MechanismDescriptionStatusParticipantsSLA
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Jurisdiction Compliance Heatmap

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AGI Governance Master Blueprint — Enterprise · Frontier · Civilizational

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AGI Governance Master Blueprint — Enterprise + Frontier + Civilizational Scale

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Three-Scale Governance Architecture

Scalability Pathway

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LevelScopeGovernanceInvestmentTimeline
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Integration with Existing Frameworks

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FrameworkIntegrationTouchpoints
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LevelScopeGovernance ModelInvestmentTimelineMilestone
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Framework Integration Matrix

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FrameworkIntegration ApproachTouchpointsCoverage
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Implementation Timelines, Risk Assessment & Cost-Benefit Analysis

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Implementation Roadmap — Timelines, Risk Register & Financial Analysis

5-Phase Implementation Timeline (2026-2030)

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Investment Breakdown

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Category5-Year CostNPVIRR
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Annual Savings Breakdown

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CategoryAnnual SavingDescription
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Top Risks

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IDRiskProbabilityImpactMitigationOwner
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30/60/90-Day Rollout

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Investment Breakdown by Category

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Category5-Year CostNPVIRRPayback
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Annual Savings Breakdown — $52.3M

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CategoryAnnual SavingDescription
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Risk Register — Top Risks by Severity

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IDRiskProbabilityImpactSeverityMitigationOwner
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30/60/90-Day Rapid Deployment Checklist

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Appendices — Templates, Checklists & Reference Materials

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Templates

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IDNameFormatPath
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Checklists

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IDNameItemsCovers
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Reference Materials

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RefTitlePath
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Appendices — Templates, Checklists, OPA Policies & Reference Materials

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Templates (10)

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IDNameFormatArtifact PathStandard
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Regulator-Ready Checklists (5)

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IDNameItemsRegulatory Coverage
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OPA Policy Catalog — 14 Rego Policy Files

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Companion Document Registry — 22 Whitepapers

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RefTitleVersionStatus
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API Explorer — 81 Master Reference Endpoints

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Endpoint Catalog by Domain

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MREF-GSIFI-WP-023 CONFIDENTIAL
+ diff --git a/rag-agentic-dashboard/public/six-layer-governance.html b/rag-agentic-dashboard/public/six-layer-governance.html new file mode 100644 index 0000000..431f393 --- /dev/null +++ b/rag-agentic-dashboard/public/six-layer-governance.html @@ -0,0 +1,1122 @@ + + + + +GSIFI-REFARCH-WP-024 — Six-Layer Full-Stack AI Governance for Tier-1 Global Banks + + + + +
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Enterprise AI Governance Reference Architecture

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+ GSIFI-REFARCH-WP-024 | + v1.0.0 | + 2026-04-10 | + CONFIDENTIAL + CONNECTING +
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Six-Layer Full-Stack Governance Model for AGI-Capable Systems in Tier-1 Global Banks
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+ API Connected + Endpoints: -- + Layers: -- + Controls: -- + Mappings: -- + KPIs: -- + Updated: -- +
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Executive Dashboard

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Regulatory Framework Coverage

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Governance Layers — KPI Achievement
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Investment Breakdown by Phase
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Document Classification

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Companion Documents

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Six-Layer Full-Stack AI Governance Model

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Layer Detail

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Regulatory Mapping by Layer

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KPI Summary Across All Layers

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Three Lines of Defense — AI/AGI Governance

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CAIGO Authority Profile

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AI Risk Committee Composition

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Specialized Governance Offices

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Minimal Enterprise AI Governance Stack

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Stack Maturity Overview

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Regulatory Control Crosswalk

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Controls
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Mappings
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Frameworks
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Full Crosswalk Matrix

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Evidence Artifacts & Auditor Questions

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CI/CD Governance Pipeline — 7-Stage Human-in-the-Loop

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Gate Details

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Runtime Escalation Thresholds

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Data Governance Fields in Model Registry

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Total Fields
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Required
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90-Day MVP Implementation Roadmap

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4
Phases
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90
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Year-1 Investment
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Team FTE
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Go/No-Go Gates

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AI Crisis Simulation Scenarios

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Phase 4 of the 90-day MVP: Three production-grade crisis tabletop exercises designed to stress-test the governance architecture under extreme conditions.

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Post-Simulation Hardening Actions

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Executive & Board-Ready Deliverables

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Prioritized Recommendation

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Artifact Sequencing

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Board Package Component 1: Architecture Slide (16:9 Preview)

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Board Package Component 2: One-Page Executive Briefing

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Board Package Component 3: Regulatory Crosswalk & Technical Annex

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Design Principles for Board Communications

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API Explorer — GSIFI-REFARCH-WP-024

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42 REST API endpoints powering this reference architecture. Click any endpoint to inspect the live response.

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Live API Response

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Select an endpoint above to inspect the response.
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+ + + + diff --git a/rag-agentic-dashboard/server.js b/rag-agentic-dashboard/server.js index 4d3781c..79a1bc4 100644 --- a/rag-agentic-dashboard/server.js +++ b/rag-agentic-dashboard/server.js @@ -12525,7 +12525,8 @@ app.get('/api/governance-index', (_, res) => res.json({ modules: [ { name: 'Practitioner Master Reference', api: '/api/practitioner-master-reference', dashboard: '/practitioner-master-reference.html', docRef: 'PMREF-GSIFI-WP-015', endpoints: 50 }, { name: 'AGI Governance Master Blueprint', api: '/api/agi-governance-master-blueprint', dashboard: '/agi-governance-master-blueprint.html', docRef: 'AGMB-GSIFI-WP-016', endpoints: 39 }, - { name: 'Governance Architectures & Frameworks', api: '/api/governance-architectures-frameworks', dashboard: '/governance-architectures-frameworks.html', docRef: 'GAF-GSIFI-WP-017', endpoints: 57 } + { name: 'Governance Architectures & Frameworks', api: '/api/governance-architectures-frameworks', dashboard: '/governance-architectures-frameworks.html', docRef: 'GAF-GSIFI-WP-017', endpoints: 57 }, + { name: 'Six-Layer G-SIFI Reference Architecture', api: '/api/gsifi-refarch', dashboard: '/six-layer-governance.html', docRef: 'GSIFI-REFARCH-WP-024', endpoints: 42 } ], keyEndpoints: [ '/api/practitioner-master-reference/governance-layers', @@ -12709,8 +12710,8 @@ app.get('/api/governance-index', (_, res) => res.json({ { ref: 'GAF-GSIFI-WP-017', title: 'AGI/ASI Governance Architectures & Frameworks', path: '/docs/reports/AGI_ASI_GOVERNANCE_ARCHITECTURES_FRAMEWORKS.md' } ], dashboards: { - count: 35, - governance: ['/governance-index.html', '/practitioner-master-reference.html', '/agi-governance-master-blueprint.html', '/kafka-acl-governance.html', '/governance-architectures-frameworks.html', '/gsifi-governance.html', '/gsifi-practitioner-guide.html'], + count: 36, + governance: ['/governance-index.html', '/practitioner-master-reference.html', '/agi-governance-master-blueprint.html', '/kafka-acl-governance.html', '/governance-architectures-frameworks.html', '/gsifi-governance.html', '/gsifi-practitioner-guide.html', '/six-layer-governance.html'], strategy: ['/enterprise-ai-strategy-g2k.html', '/master-reference.html', '/unified-master-reference.html', '/ai-strategy-report.html'], safety: ['/agi-governance.html', '/asi-preparedness.html', '/agi-governance-unified.html'], platform: ['/index.html', '/eaip-specification.html', '/ciso-roadmap.html', '/ciso-report.html'], @@ -15094,6 +15095,102 @@ app.get('/api/master-ref/appendices/templates', (_, res) => res.json(MASTER_REF. app.get('/api/master-ref/appendices/checklists', (_, res) => res.json(MASTER_REF.appendices.checklists)); app.get('/api/master-ref/appendices/reference-materials', (_, res) => res.json(MASTER_REF.appendices.referenceMaterials)); +// Additional master-ref endpoints for comprehensive coverage +app.get('/api/master-ref/regulatory/policy-as-code', (_, res) => res.json({ + totalPolicies: 482, framework: 'OPA Rego', engine: 'Open Policy Agent v0.60+', + policyFiles: ['eu_ai_act_high_risk.rego','nist_ai_rmf_govern.rego','iso42001_aims_governance.rego','gdpr_ai_data_protection.rego','fair_lending_disparate_impact.rego','basel_iii_model_risk.rego','sr_11_7_model_validation.rego','kafka_acl_governance.rego','eu_ai_act_kafka_enforcement.rego','agent_governance_depths.rego','development_deployment_governance.rego','monitoring_sentinel_engine.rego','oecd_ai_principles.rego','master_reference_compliance.rego'], + evaluationsPerDay: '1.4M', p99Latency: '4.2ms', availability: '99.97%' +})); +app.get('/api/master-ref/governance-structure/raci-matrix', (_, res) => res.json({ + roles: ['Board','CAIO','CRO','CISO','CTO','Legal','MRM','DevOps','Audit'], + activities: [ + {activity:'AI System Registration',raci:['A','R','C','I','C','C','I','I','I']}, + {activity:'Model Risk Assessment',raci:['I','C','R','C','I','I','A','I','C']}, + {activity:'Policy Deployment',raci:['I','A','C','C','R','I','I','R','I']}, + {activity:'Compliance Monitoring',raci:['I','A','R','C','I','C','C','I','C']}, + {activity:'Incident Response',raci:['I','A','R','R','R','C','I','R','I']}, + {activity:'AGI Safety Review',raci:['A','R','R','R','C','C','C','I','C']}, + {activity:'Regulatory Reporting',raci:['A','C','R','I','I','R','C','I','R']}, + {activity:'Kill-Switch Activation',raci:['I','A','R','R','R','I','I','R','I']}, + {activity:'Annual Audit',raci:['A','C','C','C','C','C','C','I','R']} + ] +})); +app.get('/api/master-ref/technical/kafka-acl/acl-rules', (_, res) => res.json({ + totalRules: 312, ruleGroups: 11, + enforcement: 'mTLS + SPIFFE SVIDs', auditRetention: '10 years', + topicCount: MASTER_REF.technicalImplementation.kafkaAclGovernance.topics.length, + throughput: '45,000 events/sec', availability: '99.997%' +})); +app.get('/api/master-ref/technical/worm-storage', (_, res) => res.json({ + type: 'S3-compatible WORM', signing: 'SHA-256 + Ed25519', + retentionMinimum: '10 years', evidenceBundleP99: '4.8s', + evidenceAssemblyReduction: '94%', assemblyTimeBefore: '72h', assemblyTimeAfter: '4.3h', + storageFormat: 'Immutable append-only', verificationTool: 'governance-verify-cli.py' +})); +app.get('/api/master-ref/technical/drift-detection', (_, res) => res.json({ + enabled: true, interval: '15 minutes', engine: 'Terraform + Sentinel', + autoRemediation: true, driftCategories: ['ACL Configuration','Policy Version','Schema Registry','Certificate Rotation','Retention Policy'], + alertThreshold: 'Any unauthorized change', escalation: 'Immediate SEV-2' +})); +app.get('/api/master-ref/technical/evidence-bundles', (_, res) => res.json({ + generationP99: '4.8s', formats: ['SR 11-7','EU AI Act Art.11','ISO 42001','Basel III CRE 30-36'], + bundleTypes: ['Compliance Assessment','Model Validation','Incident Response','Audit Evidence','Regulatory Submission'], + storageIntegrity: 'SHA-256 chain-of-custody', retrieval: 'Self-service portal + API' +})); +app.get('/api/master-ref/financial-services/risk-management', (_, res) => res.json({ + category: 'Risk Management Models', models: 94, production: 42, tier: 'Tier-2', + srSection: 'SR 11-7 §IV', pdAccuracy: '0.91', framework: 'Basel III CRE 30-36', + validationFrequency: 'Annual + trigger-based' +})); +app.get('/api/master-ref/financial-services/customer-service', (_, res) => res.json({ + category: 'Customer Service AI', models: 67, production: 28, tier: 'Tier-2', + srSection: 'SR 11-7 §V', avgCSAT: '4.2/5.0', framework: 'Consumer Duty + FCRA', + complianceScore: '94.1%' +})); +app.get('/api/master-ref/agi-safety/kill-switch/status', (_, res) => res.json({ + status: 'ARMED', lastTest: '2026-04-01T00:00:00Z', testResult: 'PASS', + activationLatency: '<100ms', types: ['Hardware Kill-Switch','Software Kill-Switch','Network Isolation','Resource Throttle'], + authority: ['CAIO','CRO','Board Chair'], requiresDualApproval: true +})); +app.get('/api/master-ref/agi-safety/cognitive-resonance', (_, res) => res.json({ + protocol: 'Cognitive Resonance Protocol v1.0', status: 'Deployed', + components: ['Alignment Monitor','Value Drift Detector','Goal Stability Verifier','Reward Hacking Guard'], + testSuite: 847, passRate: '97.2%', deployedSince: 'Q2 2026' +})); +app.get('/api/master-ref/global-governance/jurisdiction-compliance', (_, res) => res.json({ + jurisdictions: [ + {name:'United States',score:94.8,frameworks:['NIST AI RMF','FCRA/ECOA','SR 11-7']}, + {name:'European Union',score:89.4,frameworks:['EU AI Act','GDPR','ISO 42001']}, + {name:'United Kingdom',score:91.2,frameworks:['UK AI Framework','UK GDPR','PRA SS1/23']}, + {name:'OECD Global',score:91.6,frameworks:['OECD AI Principles','ISO 42001']} + ], + overallAverage: 91.75 +})); +app.get('/api/master-ref/blueprint/unified-view', (_, res) => res.json({ + scales: MASTER_REF.masterBlueprint.scales, + scalability: MASTER_REF.masterBlueprint.scalabilityPathway, + integration: MASTER_REF.masterBlueprint.integrationWithExisting, + unifiedThesis: 'Enterprise + Frontier + Civilizational governance unified under MREF-GSIFI-WP-023' +})); +app.get('/api/master-ref/implementation/risks/register', (_, res) => res.json({ + totalRisks: MASTER_REF.implementation.riskAssessment.totalRisks, + criticalRisks: MASTER_REF.implementation.riskAssessment.criticalRisks, + highRisks: MASTER_REF.implementation.riskAssessment.highRisks, + risks: MASTER_REF.implementation.riskAssessment.topRisks +})); +app.get('/api/master-ref/implementation/kpi-targets', (_, res) => res.json({ + targets: [ + {kpi:'Regulatory Compliance Score',baseline:'72.4%',target2026:'91.2%',target2028:'97.1%',target2030:'99.2%'}, + {kpi:'OPA Policy Coverage',baseline:'124',target2026:'482',target2028:'850',target2030:'1,200+'}, + {kpi:'Sentinel Rules',baseline:'312',target2026:'1,247',target2028:'2,000',target2030:'2,800+'}, + {kpi:'Daily Policy Evaluations',baseline:'280K',target2026:'1.4M',target2028:'5.5M',target2030:'8M+'}, + {kpi:'Mean Incident Response',baseline:'45 min',target2026:'14 min',target2028:'5 min',target2030:'3 min'}, + {kpi:'AI Risk Score',baseline:'38.2',target2026:'55.8',target2028:'75.2',target2030:'82.5'}, + {kpi:'Model Bias DI',baseline:'0.72',target2026:'0.80',target2028:'0.88',target2030:'0.92'}, + {kpi:'AGI Readiness Level',baseline:'ARL-1',target2026:'ARL-2',target2028:'ARL-5',target2030:'ARL-7'} + ] +})); + // Dashboard / KPI Summary app.get('/api/master-ref/dashboard', (_, res) => { const fwScores = MASTER_REF.regulatoryCompliance.frameworks.map(f => ({ id: f.id, name: f.name, score: f.complianceScore })); @@ -15140,6 +15237,839 @@ app.get('/api/master-ref/metrics', (_, res) => { }); }); +// ══════════════════════════════════════════════════════════════════════════════ +// SECTION: G-SIFI REFERENCE ARCHITECTURE — ENTERPRISE AI GOVERNANCE +// Document: GSIFI-REFARCH-WP-024 v1.0.0 +// Scope: Tier-1 Global Banks, G-SIFIs, AGI-Capable System Governance +// Six-Layer Full-Stack Model · Three-Lines-of-Defense · Regulatory Crosswalk +// Board Deliverables · 90-Day MVP Roadmap · Crisis Simulation Scenarios +// ══════════════════════════════════════════════════════════════════════════════ + +const GSIFI_REFARCH = { + meta: { + documentReference: 'GSIFI-REFARCH-WP-024', + title: 'Enterprise AI Governance Reference Architecture for Global Systemically Important Financial Institutions', + subtitle: 'Six-Layer Full-Stack Governance Model for AGI-Capable Systems in Tier-1 Global Banks', + version: '1.0.0', + date: '2026-04-10', + classification: 'CONFIDENTIAL — Board Risk Committee / C-Suite / Prudential Supervisors / Internal Audit', + authors: [ + 'Chief AI Governance Officer (CAIGO)', + 'Chief Risk Officer', + 'Chief Information Security Officer', + 'Head of Model Risk Management', + 'VP AI Ethics & Safety', + 'Head of Data Governance', + 'Chief Technology Officer', + 'General Counsel', + 'Head of Compute Governance' + ], + audience: [ + 'Board Risk Committee', + 'Board Technology Committee', + 'Group CEO / Group CRO / Group CTO / Group CISO', + 'Prudential Supervisors (Fed, PRA, ECB-SSM, MAS)', + 'AI Risk Committee', + 'Model Risk Management', + 'Internal Audit / External Audit', + 'Financial Conduct Regulators (FCA, CFPB, ESMA)' + ], + applicability: 'All AI/ML systems deployed by the institution, with enhanced controls for AGI-capable, autonomous-decision, and frontier-model systems', + regulatoryFrameworks: ['EU AI Act (2024/1689)', 'NIST AI RMF 1.0', 'ISO/IEC 42001:2023', 'SR 11-7', 'GDPR (2016/679)', 'FCRA/ECOA', 'Basel III CRE 30-36', 'PRA SS1/23', 'FCA PS23/16', 'MAS FEAT'], + supersedes: ['GSIFI-REFARCH-WP-024-DRAFT-001'], + companionDocuments: [ + { ref: 'MREF-GSIFI-WP-023', title: 'Master Reference 2026-2030' }, + { ref: 'KACG-GSIFI-WP-017', title: 'Kafka ACL Governance Engine' }, + { ref: 'PRACT-GSIFI-WP-011', title: 'Practitioner G-SIFI Guide' }, + { ref: 'SAFE-AGI-WP-003', title: 'AGI Readiness & Safety Frameworks' } + ] + }, + + // ════════════════════════════════════════════════════════════ + // SIX-LAYER FULL-STACK AI GOVERNANCE MODEL + // ════════════════════════════════════════════════════════════ + sixLayerModel: { + designPrinciple: 'Defense-in-depth governance: every AI decision traverses six explicit control layers from board mandate to silicon. No layer may be bypassed; each produces immutable evidence.', + layers: [ + { + id: 'L1', name: 'Board & Enterprise Risk Oversight', + function: 'Strategic direction, risk appetite, regulatory accountability, fiduciary duty for AI/AGI systems', + owner: 'Board Risk Committee + Board Technology Sub-Committee', + threeLines: '3rd Line + Board Oversight', + controls: [ + 'AI/AGI risk appetite statement (annual, board-approved)', + 'Quarterly AI risk dashboard review with traffic-light escalation', + 'Annual AGI readiness stress test and tabletop exercise', + 'Board-level kill-switch authorization protocol', + 'SMCR/SIMR accountability mapping for AI decisions', + 'Regulatory examination response ownership' + ], + keyRoles: ['Board Risk Committee Chair', 'Non-Executive AI Director', 'Group CEO', 'Group CRO'], + artifacts: ['AI Risk Appetite Statement', 'Board AI Dashboard', 'Annual AI Attestation', 'Crisis Playbook'], + regulatoryMapping: { 'EU AI Act': 'Art. 9, 26 (deployer obligations)', 'NIST AI RMF': 'GOVERN 1-6', 'ISO 42001': 'Clause 5 (Leadership)', 'SR 11-7': 'Board oversight mandate', 'GDPR': 'Art. 35 (DPIA oversight)' }, + maturityTarget: 'Level 4 (Proactive) by Q4 2027', + kpis: [ + { kpi: 'Board AI dashboard review cadence', target: 'Quarterly', current: 'Quarterly' }, + { kpi: 'AI risk appetite refresh', target: 'Annual', current: 'Annual' }, + { kpi: 'Board tabletop exercise completion', target: '1/year', current: '1/year' }, + { kpi: 'SMCR mapping coverage', target: '100%', current: '94%' } + ] + }, + { + id: 'L2', name: 'AI Strategy & Policy Infrastructure', + function: 'Enterprise AI policies, standards, taxonomies, ethical frameworks, and responsible AI principles translated into enforceable rules', + owner: 'CAIGO + AI Risk Committee', + threeLines: '2nd Line (AI Risk)', + controls: [ + 'Enterprise AI Policy (Tier-1, board-approved annually)', + 'AI Standards Library (Tier-2, CRO-approved quarterly)', + 'Risk classification taxonomy: Prohibited → High → Limited → Minimal', + 'Ethical AI principles (fairness, transparency, accountability, safety, privacy)', + 'Policy-as-code enforcement via OPA Rego (482+ rules)', + 'Policy exception register with time-bound expiry and CRO escalation', + 'Cross-jurisdictional policy harmonization matrix' + ], + keyRoles: ['CAIGO', 'AI Risk Committee Chair', 'VP AI Ethics & Safety', 'General Counsel'], + artifacts: ['Enterprise AI Policy v3.0', 'AI Standards Library', 'Risk Taxonomy', 'Policy Exception Register', 'OPA Rule Catalog'], + regulatoryMapping: { 'EU AI Act': 'Art. 6-7 (risk classification), Art. 9 (risk management)', 'NIST AI RMF': 'GOVERN, MAP', 'ISO 42001': 'Clause 6 (Planning), Clause 7 (Support)', 'SR 11-7': 'Policy framework requirements', 'GDPR': 'Art. 5, 25 (data protection by design)' }, + maturityTarget: 'Level 4 (Proactive) by Q2 2027', + kpis: [ + { kpi: 'Policy coverage (AI systems governed)', target: '100%', current: '91%' }, + { kpi: 'OPA rule coverage (regulatory requirements)', target: '95%', current: '88%' }, + { kpi: 'Policy exception age (avg days)', target: '<90', current: '67' }, + { kpi: 'Cross-jurisdictional alignment', target: '95%', current: '86%' } + ] + }, + { + id: 'L3', name: 'Model Lifecycle & Risk Management', + function: 'End-to-end model lifecycle governance from ideation through decommission, including risk assessment, validation, monitoring, and MRM', + owner: 'Head of Model Risk Management + CAIGO', + threeLines: '2nd Line (Model Risk)', + controls: [ + 'AI System Inventory Registry (all models, all jurisdictions)', + 'Risk classification engine: 12-dimension scoring (ARS v2.0)', + 'Model validation platform (independent challenger, back-testing)', + 'SR 11-7 compliant validation lifecycle (6-stage pipeline)', + 'Fairness testing: disparate impact ratio ≥ 0.80 threshold', + 'Model performance monitoring (drift detection, degradation alerting)', + 'Model decommission protocol with evidence archival', + 'Frontier model enhanced validation (AAVP: 2,847 tests)' + ], + keyRoles: ['Head of MRM', 'Lead Model Validators', 'Model Risk Analysts', 'AI Safety Engineers'], + artifacts: ['Model Inventory', 'Model Risk Assessment', 'Validation Report', 'Performance Monitor Dashboard', 'Decommission Certificate'], + regulatoryMapping: { 'EU AI Act': 'Art. 9-15 (high-risk requirements)', 'NIST AI RMF': 'MAP, MEASURE', 'ISO 42001': 'Clause 8 (Operation), Annex A', 'SR 11-7': 'Full lifecycle (§§III-V)', 'GDPR': 'Art. 22 (automated decision-making)', 'FCRA/ECOA': 'Fair lending model validation' }, + maturityTarget: 'Level 4 (Proactive) by Q4 2026', + kpis: [ + { kpi: 'Model inventory completeness', target: '100%', current: '96%' }, + { kpi: 'Validation currency (models within cycle)', target: '95%', current: '89%' }, + { kpi: 'Avg validation turnaround (days)', target: '<45', current: '52' }, + { kpi: 'Disparate impact compliance', target: '100% ≥ 0.80', current: '97%' }, + { kpi: 'High-risk model re-validation frequency', target: 'Annual', current: 'Annual' } + ] + }, + { + id: 'L4', name: 'Data Governance & Privacy Engineering', + function: 'AI-ready data infrastructure: lineage, quality, consent, privacy, PII protection, and cross-border data transfer governance', + owner: 'Chief Data Officer + Data Protection Officer', + threeLines: '1st Line (Data Operations) + 2nd Line (Data Governance)', + controls: [ + 'Data lineage tracking (source → feature → model → output)', + 'Data quality gates: 6 dimensions (completeness, accuracy, timeliness, consistency, uniqueness, validity)', + 'Overall data quality score threshold ≥ 0.85', + 'PII detection and classification (99.7% accuracy)', + 'GDPR Art. 17 erasure compliance (automated, < 72h)', + 'Cross-border data transfer impact assessment', + 'Consent management for AI training data', + 'Synthetic data governance for model development', + 'Data governance fields in model registry (14 mandatory fields)' + ], + keyRoles: ['CDO', 'DPO', 'Data Stewards', 'Privacy Engineers', 'Data Quality Analysts'], + artifacts: ['Data Lineage Maps', 'Data Quality Reports', 'DPIA Register', 'Consent Records', 'Erasure Audit Trail'], + regulatoryMapping: { 'EU AI Act': 'Art. 10 (data governance), Art. 12 (record-keeping)', 'NIST AI RMF': 'MAP 2, MEASURE 2', 'ISO 42001': 'Annex A.6 (Data)', 'SR 11-7': 'Data validation requirements', 'GDPR': 'Art. 5-9, 13-22, 25, 30, 35' }, + maturityTarget: 'Level 3 (Defined) by Q2 2027', + modelRegistryDataFields: [ + { field: 'data_sources', type: 'array', required: true, description: 'List of all data sources with lineage IDs' }, + { field: 'training_data_version', type: 'string', required: true, description: 'Immutable version hash of training dataset' }, + { field: 'pii_classes_present', type: 'array', required: true, description: 'PII categories in training/inference data' }, + { field: 'consent_basis', type: 'enum', required: true, description: 'Legal basis: consent | legitimate_interest | contract | legal_obligation' }, + { field: 'data_quality_score', type: 'float', required: true, description: 'Composite DQ score (0.0-1.0), minimum 0.85' }, + { field: 'cross_border_transfers', type: 'array', required: false, description: 'Jurisdictions where data is transferred' }, + { field: 'retention_policy', type: 'string', required: true, description: 'Data retention period and deletion trigger' }, + { field: 'synthetic_data_ratio', type: 'float', required: false, description: 'Proportion of synthetic vs real data' }, + { field: 'bias_audit_date', type: 'date', required: true, description: 'Last bias/fairness audit of training data' }, + { field: 'feature_store_ref', type: 'string', required: false, description: 'Reference to centralized feature store' }, + { field: 'dpia_ref', type: 'string', required: true, description: 'Data Protection Impact Assessment reference' }, + { field: 'data_steward', type: 'string', required: true, description: 'Accountable data steward name/ID' }, + { field: 'encryption_at_rest', type: 'boolean', required: true, description: 'AES-256 encryption status' }, + { field: 'anonymization_method', type: 'string', required: false, description: 'Anonymization/pseudonymization technique applied' } + ], + kpis: [ + { kpi: 'Data quality score (composite)', target: '≥ 0.85', current: '0.87' }, + { kpi: 'PII detection accuracy', target: '99.5%', current: '99.7%' }, + { kpi: 'Erasure request compliance (< 72h)', target: '100%', current: '99.4%' }, + { kpi: 'Data lineage coverage', target: '95%', current: '82%' }, + { kpi: 'Model registry data field completeness', target: '100%', current: '91%' } + ] + }, + { + id: 'L5', name: 'Development, Deployment & Runtime Governance', + function: 'CI/CD pipeline governance, human-in-the-loop gates, runtime monitoring, incident response, escalation thresholds, and operational controls', + owner: 'CTO + Head of AI Platform Engineering', + threeLines: '1st Line (Engineering)', + controls: [ + 'CI/CD governance pipeline: 7-stage with human-in-the-loop gates', + 'Runtime monitoring: real-time performance, drift, fairness, safety', + 'Tamper-evident audit logging (Kafka WORM, SHA-256 + Ed25519)', + 'KPI/SLA oversight panel with automated alerting', + 'Incident response ownership matrix (RACI per severity)', + 'Runtime escalation thresholds (5 trigger types)', + 'Canary/blue-green deployment with automated rollback', + 'Kill-switch architecture (hardware + software + network isolation)', + 'AI system health dashboard (real-time, board-accessible)' + ], + keyRoles: ['CTO', 'Head AI Platform', 'MLOps Engineers', 'SRE/DevSecOps', 'Incident Commanders'], + artifacts: ['CI/CD Gate Reports', 'Runtime Dashboards', 'Audit Logs', 'Incident Reports', 'Deployment Certificates'], + cicdGates: [ + { gate: 1, name: 'Code Quality & Security Scan', tool: 'SonarQube + Snyk + Semgrep', humanApproval: false, blocksOn: 'Critical/High vulns, code coverage < 80%' }, + { gate: 2, name: 'Data Validation & DQ Check', tool: 'Great Expectations + Custom DQ Engine', humanApproval: false, blocksOn: 'DQ score < 0.85, schema drift, PII leak' }, + { gate: 3, name: 'Model Performance Validation', tool: 'MLflow + Custom Benchmark Suite', humanApproval: true, blocksOn: 'AUC/F1 regression > 2%, latency > SLA' }, + { gate: 4, name: 'Bias & Fairness Assessment', tool: 'Fairlearn + AIF360 + Custom DI Engine', humanApproval: true, blocksOn: 'DI < 0.80, protected-class disparity > 5%' }, + { gate: 5, name: 'Policy-as-Code Compliance', tool: 'OPA Rego (482 rules) + Sentinel (1,247 rules)', humanApproval: false, blocksOn: 'Any policy violation (zero-tolerance for High-Risk)' }, + { gate: 6, name: 'Security & Adversarial Testing', tool: 'Garak + Custom Red-Team Suite + PenTest', humanApproval: true, blocksOn: 'Prompt injection vuln, jailbreak success > 0.1%' }, + { gate: 7, name: 'Deployment Approval & Sign-off', tool: 'Governance Portal + MRM Sign-off', humanApproval: true, blocksOn: 'Missing validation cert, risk owner approval, or CAIGO sign-off for High-Risk' } + ], + runtimeEscalationThresholds: [ + { trigger: 'Performance Degradation', metric: 'AUC/F1 drop > 5% from baseline over 24h rolling window', severity: 'SEV-2', escalation: 'Model Owner → MRM → CAIGO', sla: '4 hours', autoAction: 'Traffic throttle to 50%, shadow mode activation' }, + { trigger: 'Fairness Drift', metric: 'DI ratio drops below 0.80 for any protected class', severity: 'SEV-1', escalation: 'AI Ethics → CRO → Board Risk Committee (if systemic)', sla: '2 hours', autoAction: 'Immediate model quarantine for credit/lending models' }, + { trigger: 'Data Quality Breach', metric: 'Input DQ score < 0.80 or schema violation rate > 1%', severity: 'SEV-2', escalation: 'Data Steward → CDO → Model Owner', sla: '4 hours', autoAction: 'Fallback to last-known-good data pipeline' }, + { trigger: 'Adversarial/Anomalous Input', metric: 'Anomaly score > 3σ or known attack pattern detected', severity: 'SEV-1', escalation: 'CISO SOC → AI Security → CAIGO → Kill-switch if autonomous', sla: '1 hour', autoAction: 'Rate limit + enhanced logging + human review queue' }, + { trigger: 'Autonomous Decision Volume Spike', metric: 'Decision throughput > 200% of 30-day average', severity: 'SEV-2', escalation: 'Operations → CRO Risk → CAIGO', sla: '2 hours', autoAction: 'Throttle to baseline, require human approval above threshold' } + ], + regulatoryMapping: { 'EU AI Act': 'Art. 9 (risk management), Art. 14 (human oversight), Art. 72 (post-market monitoring)', 'NIST AI RMF': 'MANAGE 1-4', 'ISO 42001': 'Clause 8, 9, 10', 'SR 11-7': '§IV-V (ongoing monitoring)', 'GDPR': 'Art. 32 (security), Art. 33-34 (breach notification)' }, + maturityTarget: 'Level 4 (Proactive) by Q2 2027', + kpis: [ + { kpi: 'CI/CD pipeline pass rate', target: '95%', current: '92%' }, + { kpi: 'Mean time to detect (MTTD)', target: '< 15 min', current: '23 min' }, + { kpi: 'Mean time to respond (MTTR)', target: '< 60 min', current: '87 min' }, + { kpi: 'Audit log integrity verification', target: '100%', current: '100%' }, + { kpi: 'Runtime escalation SLA compliance', target: '98%', current: '91%' }, + { kpi: 'Human-in-the-loop gate coverage', target: '100% for High-Risk', current: '100%' } + ] + }, + { + id: 'L6', name: 'Compute & Infrastructure Governance', + function: 'Hardware/infrastructure controls: compute allocation, GPU cluster governance, energy monitoring, physical security, supply chain, and sovereign compute compliance', + owner: 'Head of Compute Governance + CISO', + threeLines: '1st Line (Infrastructure) + 2nd Line (Security)', + controls: [ + 'Compute allocation registry (GPU/TPU inventory, utilization, access)', + 'Training compute threshold monitoring (frontier model detection)', + 'Sovereign compute compliance (data residency, jurisdictional boundaries)', + 'Hardware security module (HSM) key management for model signing', + 'Supply chain governance for AI accelerators (provenance, sanctions)', + 'Energy and carbon footprint monitoring for AI workloads', + 'Physical security for AI training clusters (SCIF-equivalent)', + 'Network segmentation for AI workloads (Zero Trust Architecture)', + 'Compute cost allocation and chargeback governance' + ], + keyRoles: ['Head of Compute Governance', 'CISO', 'VP Infrastructure', 'Cloud Security Architects', 'Procurement AI Hardware'], + artifacts: ['Compute Registry', 'Utilization Reports', 'Sovereignty Assessment', 'HSM Key Inventory', 'Carbon Reports'], + regulatoryMapping: { 'EU AI Act': 'Art. 52a (compute thresholds for GPAI)', 'NIST AI RMF': 'GOVERN 5 (resource allocation)', 'ISO 42001': 'Annex A.8 (Technology)', 'SR 11-7': 'Infrastructure risk for model-dependent systems', 'GDPR': 'Art. 32 (appropriate technical measures)' }, + maturityTarget: 'Level 3 (Defined) by Q4 2027', + kpis: [ + { kpi: 'Compute registry completeness', target: '100%', current: '78%' }, + { kpi: 'Sovereign compute compliance', target: '100%', current: '94%' }, + { kpi: 'GPU utilization efficiency', target: '> 70%', current: '62%' }, + { kpi: 'HSM key rotation compliance', target: '100%', current: '100%' }, + { kpi: 'Carbon reporting coverage', target: '100%', current: '45%' } + ] + } + ] + }, + + // ════════════════════════════════════════════════════════════ + // THREE LINES OF DEFENSE + KEY GOVERNANCE ROLES + // ════════════════════════════════════════════════════════════ + threeLinesOfDefense: { + model: 'Enhanced Three Lines of Defense adapted for AI/AGI governance in G-SIFIs, with additional Board Oversight layer', + lines: [ + { + line: '1st Line', name: 'AI Development & Operations', + function: 'Build, deploy, operate, and monitor AI systems within policy guardrails', + roles: [ + { title: 'AI/ML Engineers', count: '200-400 FTE (typical Tier-1)', responsibility: 'Build models within governance frameworks' }, + { title: 'MLOps/DevSecOps', count: '50-100 FTE', responsibility: 'CI/CD pipeline operation and gate enforcement' }, + { title: 'Data Engineers', count: '100-200 FTE', responsibility: 'Data pipeline quality and lineage' }, + { title: 'SRE/Infrastructure', count: '30-60 FTE', responsibility: 'Runtime reliability and compute governance' } + ], + accountabilities: ['Policy compliance in daily operations', 'Self-assessment and testing', 'Incident first-response', 'Evidence production for audit'] + }, + { + line: '2nd Line', name: 'AI Risk Management & Compliance', + function: 'Independent oversight, challenge, validation, and policy enforcement', + roles: [ + { + title: 'Chief AI Governance Officer (CAIGO)', + reportsTo: 'Group CRO (functional) / CEO (administrative)', + responsibility: 'Enterprise-wide AI governance accountability. Chairs AI Risk Committee. Owns AI policy framework, risk taxonomy, compliance-as-code program. Authority to halt any AI system deployment.', + budget: '$2.8M annual (governance operations + external advisory)', + directReports: 12, + keyAuthorities: ['AI system deployment halt', 'Risk appetite breach escalation', 'Policy exception approval/denial', 'Regulatory examination coordination', 'Kill-switch co-authorization'] + }, + { + title: 'AI Risk Committee', + chair: 'CAIGO', + members: ['CRO', 'CTO', 'CISO', 'CDO', 'General Counsel', 'Head of MRM', 'VP AI Ethics', 'Head of Compute Governance'], + cadence: 'Monthly (emergency convene within 2 hours)', + responsibilities: ['AI risk appetite monitoring', 'High-risk system deployment approval', 'Incident review and lessons learned', 'Regulatory change impact assessment', 'AGI readiness review (quarterly)'] + }, + { + title: 'AI Ethics & Safety Office', + head: 'VP AI Ethics & Safety', + reportsTo: 'CAIGO', + team: '6-10 FTE (ethicists, safety engineers, policy analysts)', + responsibilities: ['Fairness and bias testing', 'Ethical review of new use cases', 'Responsible AI principles enforcement', 'Frontier model safety assessment', 'External ethics advisory liaison', 'Consumer harm impact assessment'] + }, + { + title: 'Model Risk Management (MRM)', + head: 'Head of MRM', + reportsTo: 'CRO', + team: '40-80 FTE (model validators, quant analysts)', + responsibilities: ['Independent model validation (SR 11-7)', 'Model performance monitoring', 'Model inventory management', 'Validation methodology development', 'MRM reporting to Board Risk Committee'] + }, + { + title: 'Data Governance Office', + head: 'CDO', + reportsTo: 'COO', + team: '20-40 FTE (data stewards, DQ analysts, privacy engineers)', + responsibilities: ['Data quality framework', 'Data lineage and cataloging', 'Privacy impact assessments', 'Cross-border data compliance', 'AI training data governance'] + }, + { + title: 'Compute Governance Office', + head: 'Head of Compute Governance', + reportsTo: 'CTO', + team: '8-15 FTE (compute analysts, security architects)', + responsibilities: ['Compute allocation and registry', 'Frontier model threshold monitoring', 'Sovereign compute compliance', 'GPU/TPU supply chain governance', 'Energy and carbon reporting'] + } + ], + accountabilities: ['Independent risk assessment and challenge', 'Policy development and enforcement', 'Regulatory compliance assurance', 'Risk reporting to Board'] + }, + { + line: '3rd Line', name: 'Internal Audit & Board Oversight', + function: 'Independent assurance on governance effectiveness and regulatory compliance', + roles: [ + { title: 'AI Audit Team (within Internal Audit)', count: '8-15 FTE', responsibility: 'Independent assurance on AI governance controls, evidence verification, regulatory readiness' }, + { title: 'Board Risk Committee', cadence: 'Quarterly', responsibility: 'Strategic AI risk oversight, risk appetite approval, crisis authorization' }, + { title: 'Board Technology Sub-Committee', cadence: 'Quarterly', responsibility: 'Technology strategy alignment, compute governance oversight' } + ], + accountabilities: ['Governance design adequacy assessment', 'Control operating effectiveness testing', 'Regulatory examination readiness', 'Board-level risk reporting'] + } + ] + }, + + // ════════════════════════════════════════════════════════════ + // MINIMAL ENTERPRISE AI GOVERNANCE STACK + // ════════════════════════════════════════════════════════════ + governanceStack: { + designPrinciple: 'Eleven mandatory platform components forming the minimum viable governance stack for any G-SIFI deploying AI/AGI systems', + components: [ + { + id: 'GS-01', name: 'AI Inventory Registry', + description: 'Centralized, authoritative catalog of every AI/ML system across the institution — development, staging, production, decommissioned', + minimumFields: ['system_id', 'system_name', 'business_unit', 'risk_tier', 'model_type', 'deployment_status', 'data_sources', 'owner', 'validator', 'last_validation_date', 'regulatory_classification', 'jurisdiction', 'kill_switch_enabled'], + technology: 'Custom registry (e.g., MLflow Model Registry + governance extensions)', + layer: 'L3', regulatory: ['EU AI Act Art. 49', 'SR 11-7 §III', 'ISO 42001 A.4.3'], + currentMaturity: 'Level 3', targetMaturity: 'Level 4 by Q2 2027' + }, + { + id: 'GS-02', name: 'Risk Classification Engine', + description: '12-dimension AI Risk Score (ARS v2.0) engine that automatically classifies systems as Prohibited/High/Limited/Minimal based on use case, data sensitivity, autonomy level, and impact', + dimensions: ['Autonomy Level', 'Decision Impact', 'Data Sensitivity', 'Model Complexity', 'Regulatory Exposure', 'Consumer Impact', 'Systemic Risk', 'Explainability', 'Fairness Risk', 'Safety Criticality', 'Reversibility', 'Human Oversight Level'], + technology: 'Custom scoring engine + OPA decision trees', + layer: 'L2-L3', regulatory: ['EU AI Act Art. 6-7', 'NIST MAP 1-5', 'SR 11-7 §III'], + currentMaturity: 'Level 3', targetMaturity: 'Level 4 by Q4 2026' + }, + { + id: 'GS-03', name: 'Policy-as-Code Enforcement', + description: 'Machine-executable policy rules that automatically evaluate every AI system and deployment against regulatory and institutional requirements', + technology: 'OPA Rego (482+ rules) + Sentinel (1,247 rules)', + evaluationsPerDay: '1.4M', p99Latency: '4.2ms', availability: '99.97%', + layer: 'L2', regulatory: ['All frameworks (unified enforcement)'], + currentMaturity: 'Level 4', targetMaturity: 'Level 5 by Q4 2028' + }, + { + id: 'GS-04', name: 'Model Validation Platform', + description: 'Independent validation infrastructure for challenger testing, back-testing, stress testing, and ongoing performance monitoring per SR 11-7', + capabilities: ['Independent challenger models', 'Back-testing suites', 'Sensitivity analysis', 'Benchmark comparison', 'Stability testing', 'Fairness validation'], + technology: 'Custom validation platform + MLflow + Great Expectations', + layer: 'L3', regulatory: ['SR 11-7 §IV', 'EU AI Act Art. 9', 'ISO 42001 Clause 9'], + currentMaturity: 'Level 3', targetMaturity: 'Level 4 by Q2 2027' + }, + { + id: 'GS-05', name: 'Runtime Monitoring & Observability', + description: 'Real-time monitoring of all production AI systems: performance, fairness, drift, safety, and anomaly detection with automated alerting', + metrics: ['Inference latency', 'Prediction accuracy', 'Feature drift', 'Label drift', 'Fairness metrics', 'Anomaly scores', 'Error rates', 'Throughput', 'Resource utilization'], + technology: 'OpenTelemetry + Prometheus + Grafana + Custom AI Monitoring', + layer: 'L5', regulatory: ['EU AI Act Art. 72', 'NIST MEASURE/MANAGE', 'SR 11-7 §V'], + currentMaturity: 'Level 3', targetMaturity: 'Level 4 by Q4 2027' + }, + { + id: 'GS-06', name: 'Tamper-Evident Audit Logging', + description: 'Immutable, cryptographically-signed audit trail for every AI governance event, decision, and system interaction', + technology: 'Kafka WORM (45K events/sec) + S3 WORM + SHA-256 + Ed25519', + retention: '10 years minimum', integrity: 'Chain-of-custody verification', + layer: 'L5', regulatory: ['EU AI Act Art. 12', 'SR 11-7 §VI', 'GDPR Art. 30', 'ISO 42001 Clause 7.5'], + currentMaturity: 'Level 4', targetMaturity: 'Level 5 by Q2 2028' + }, + { + id: 'GS-07', name: 'KPI/SLA Oversight Panel', + description: 'Executive-level dashboard providing real-time visibility into AI governance health, regulatory compliance, and risk posture', + metrics: 42, refreshRate: 'Real-time (WebSocket)', audiences: ['Board', 'C-Suite', 'MRM', 'Regulators'], + layer: 'L1-L5', regulatory: ['NIST GOVERN 6', 'ISO 42001 Clause 9.1', 'SR 11-7 reporting'], + currentMaturity: 'Level 3', targetMaturity: 'Level 4 by Q2 2027' + }, + { + id: 'GS-08', name: 'Incident Response Framework', + description: 'Structured incident response with severity classification, ownership matrix, communication protocols, and post-incident review', + severityLevels: 5, avgResponseTime: '14 min (current)', targetResponseTime: '5 min (2028)', + layer: 'L5', regulatory: ['EU AI Act Art. 62', 'GDPR Art. 33-34', 'PRA SS1/23', 'SR 11-7'], + currentMaturity: 'Level 3', targetMaturity: 'Level 4 by Q2 2027' + }, + { + id: 'GS-09', name: 'CI/CD Human-in-the-Loop Gates', + description: 'Mandatory human review and approval checkpoints in the AI deployment pipeline, with authority levels tied to risk classification', + gates: 7, humanGates: 4, autoGates: 3, + layer: 'L5', regulatory: ['EU AI Act Art. 14', 'NIST GOVERN 4', 'SR 11-7 §IV'], + currentMaturity: 'Level 4', targetMaturity: 'Level 5 by Q4 2027' + }, + { + id: 'GS-10', name: 'Runtime Escalation Engine', + description: 'Automated escalation system with 5 trigger types, severity-based routing, SLA enforcement, and auto-remediation capabilities', + triggers: 5, autoActions: true, slaEnforcement: true, + layer: 'L5', regulatory: ['EU AI Act Art. 72', 'NIST MANAGE 3-4', 'SR 11-7 §V'], + currentMaturity: 'Level 3', targetMaturity: 'Level 4 by Q2 2027' + }, + { + id: 'GS-11', name: 'Data Governance Fields in Model Registry', + description: '14 mandatory data governance fields embedded in the AI model registry ensuring every model has traceable data provenance and compliance status', + mandatoryFields: 14, completionTarget: '100%', currentCompletion: '91%', + layer: 'L3-L4', regulatory: ['GDPR Art. 5,30', 'EU AI Act Art. 10', 'SR 11-7 data requirements'], + currentMaturity: 'Level 3', targetMaturity: 'Level 4 by Q4 2026' + } + ] + }, + + // ════════════════════════════════════════════════════════════ + // REGULATORY CONTROL CROSSWALK + // ════════════════════════════════════════════════════════════ + regulatoryCrosswalk: { + purpose: 'Maps each governance control to specific regulatory requirements across all applicable frameworks, with evidence artifacts regulators and internal audit will request', + frameworks: ['EU AI Act', 'NIST AI RMF', 'ISO 42001', 'SR 11-7', 'GDPR', 'FCRA/ECOA'], + totalControls: 186, + totalMappings: 847, + controls: [ + { id: 'CTRL-001', control: 'AI System Risk Classification', euAiAct: 'Art. 6-7 (risk levels)', nistRmf: 'MAP 1.1-1.6', iso42001: '6.1 (risk assessment)', sr117: '§III (model tiering)', gdpr: 'Art. 35 (DPIA trigger)', fcraEcoa: 'N/A', evidence: ['Risk assessment form', 'Classification decision record', 'ARS score calculation'], auditorQuestion: 'Show me how you classified this system as high-risk and what criteria were applied.' }, + { id: 'CTRL-002', control: 'Model Inventory & Registration', euAiAct: 'Art. 49 (EU database)', nistRmf: 'GOVERN 1.1', iso42001: 'A.4.3', sr117: '§III (inventory)', gdpr: 'Art. 30 (processing records)', fcraEcoa: 'Model documentation', evidence: ['Model registry extract', 'System inventory report', 'Jurisdiction mapping'], auditorQuestion: 'Provide a complete list of all AI models in production, their risk tiers, and validation status.' }, + { id: 'CTRL-003', control: 'Independent Model Validation', euAiAct: 'Art. 9.7 (testing)', nistRmf: 'MEASURE 1-4', iso42001: 'Clause 9.1', sr117: '§IV (effective challenge)', gdpr: 'N/A', fcraEcoa: 'Fair lending model testing', evidence: ['Validation report', 'Challenger model results', 'Back-testing analysis'], auditorQuestion: 'Walk me through the independent validation of your credit scoring model, including challenger model results.' }, + { id: 'CTRL-004', control: 'Fairness & Bias Testing', euAiAct: 'Art. 10.2(f)', nistRmf: 'MAP 2.3, MEASURE 2.6', iso42001: 'A.8.4', sr117: '§IV (outcomes analysis)', gdpr: 'Art. 22 (automated decisions)', fcraEcoa: 'DI ≥ 0.80, adverse action', evidence: ['Disparate impact report', 'Protected class analysis', 'Adverse action samples'], auditorQuestion: 'Show disparate impact ratios across all protected classes for your lending models.' }, + { id: 'CTRL-005', control: 'Human Oversight Mechanisms', euAiAct: 'Art. 14 (human oversight)', nistRmf: 'GOVERN 4', iso42001: 'A.9.2', sr117: '§V (use)', gdpr: 'Art. 22(3) (human intervention)', fcraEcoa: 'Manual review for adverse action', evidence: ['HITL gate configuration', 'Override audit trail', 'Escalation records'], auditorQuestion: 'Demonstrate that a human can intervene, override, or halt this system at any point in the decision chain.' }, + { id: 'CTRL-006', control: 'Transparency & Explainability', euAiAct: 'Art. 13 (transparency)', nistRmf: 'MAP 2.1', iso42001: 'A.7.4', sr117: '§V (documentation)', gdpr: 'Art. 13-15 (right to explanation)', fcraEcoa: 'Adverse action reasons', evidence: ['Explainability report (SHAP/LIME)', 'Consumer disclosure templates', 'Model documentation'], auditorQuestion: 'Show me how a consumer denied credit receives specific, understandable reasons for the decision.' }, + { id: 'CTRL-007', control: 'Data Governance & Quality', euAiAct: 'Art. 10 (data requirements)', nistRmf: 'MAP 2.2', iso42001: 'A.6', sr117: '§III (data validation)', gdpr: 'Art. 5 (data quality)', fcraEcoa: 'Data accuracy requirements', evidence: ['Data quality scorecard', 'Lineage documentation', 'DQ gate results'], auditorQuestion: 'Show me the data quality metrics for all input features in your trading algorithm.' }, + { id: 'CTRL-008', control: 'Continuous Monitoring & Drift Detection', euAiAct: 'Art. 72 (post-market)', nistRmf: 'MEASURE 3, MANAGE 2', iso42001: 'Clause 9.1', sr117: '§V (ongoing monitoring)', gdpr: 'Art. 32 (ongoing security)', fcraEcoa: 'Ongoing compliance monitoring', evidence: ['Monitoring dashboard screenshots', 'Drift alert history', 'Performance trend reports'], auditorQuestion: 'Show me monitoring alerts from the last quarter and how each was investigated and resolved.' }, + { id: 'CTRL-009', control: 'Incident Management & Reporting', euAiAct: 'Art. 62 (serious incidents)', nistRmf: 'MANAGE 3-4', iso42001: 'Clause 10.2', sr117: 'Incident reporting', gdpr: 'Art. 33-34 (breach notification)', fcraEcoa: 'Consumer complaint handling', evidence: ['Incident register', 'Root cause analysis', 'Regulatory notification records'], auditorQuestion: 'Provide all AI-related incidents from the past 12 months and demonstrate that each was reported within required timeframes.' }, + { id: 'CTRL-010', control: 'Audit Trail & Record-Keeping', euAiAct: 'Art. 12 (record-keeping)', nistRmf: 'GOVERN 1.5', iso42001: 'Clause 7.5', sr117: '§VI (documentation)', gdpr: 'Art. 30 (records)', fcraEcoa: 'Record retention', evidence: ['Kafka audit log samples', 'WORM storage verification', 'Chain-of-custody proof'], auditorQuestion: 'Demonstrate that your audit trail is tamper-evident and has been verified for integrity over the retention period.' }, + { id: 'CTRL-011', control: 'Kill-Switch & Emergency Shutdown', euAiAct: 'Art. 14.4(e) (stop button)', nistRmf: 'MANAGE 4', iso42001: 'A.9.5', sr117: 'Operational risk controls', gdpr: 'N/A', fcraEcoa: 'N/A', evidence: ['Kill-switch test results', 'Activation latency metrics', 'Authorization protocol documentation'], auditorQuestion: 'Show me the last kill-switch test result and demonstrate that the system can be halted within 100ms.' }, + { id: 'CTRL-012', control: 'Board & Executive Reporting', euAiAct: 'Art. 26 (deployer duties)', nistRmf: 'GOVERN 5-6', iso42001: 'Clause 9.3 (management review)', sr117: 'Board reporting mandate', gdpr: 'Art. 37-39 (DPO duties)', fcraEcoa: 'Compliance reporting', evidence: ['Board AI dashboard', 'Quarterly risk report', 'Annual attestation'], auditorQuestion: 'Show me the Board Risk Committee minutes where AI risk was discussed and what actions were taken.' } + ] + }, + + // ════════════════════════════════════════════════════════════ + // EXECUTIVE & BOARD-READY DELIVERABLES + // ════════════════════════════════════════════════════════════ + boardDeliverables: { + prioritizedRecommendation: { + recommendation: 'PRODUCE THE FULL REFERENCE ARCHITECTURE FIRST', + rationale: [ + '1. The reference architecture is the foundational artifact from which all others derive — without it, the crosswalk has no control inventory to map, and the risk taxonomy has no containment structure to reference.', + '2. Prudential supervisors (Fed, PRA, ECB-SSM) invariably ask "Show me your governance architecture" as the first examination question. The architecture diagram with ownership column is the single most-requested artifact.', + '3. The six-layer model provides the structural scaffold for the 90-day MVP: each implementation phase maps directly to specific layers, enabling traceable progress reporting.', + '4. The architecture creates organizational clarity — it defines who owns what, resolves ambiguity between 1st/2nd/3rd line for AI, and establishes the CAIGO authority mandate.', + '5. EU AI Act conformity assessment (Art. 43) requires a documented governance system before individual control testing begins.' + ], + sequencing: [ + { order: 1, artifact: 'Full Reference Architecture (6-Layer + 3LoD)', timeline: 'Week 1-3', reason: 'Foundation for all downstream artifacts' }, + { order: 2, artifact: 'EU AI Act → NIST → ISO 42001 → SR 11-7 Regulatory Crosswalk', timeline: 'Week 3-6', reason: 'Maps architecture controls to regulatory obligations' }, + { order: 3, artifact: 'Frontier-AI Risk Taxonomy & Stress-Test Scenarios', timeline: 'Week 6-10', reason: 'Extends architecture for AGI-specific risks; requires architecture as baseline' } + ] + }, + + boardPackageGuidance: { + overview: 'Three-document board package for Board Risk Committee presentation', + components: [ + { + id: 'BP-01', + name: '16:9 Architecture Slide with Ownership Column', + format: '16:9 PowerPoint / Keynote (single slide)', + layout: { + leftColumn: 'Six-layer governance model (L1 → L6) with color-coded risk tiers', + centerColumn: 'Key controls per layer (3-4 bullets each, action-oriented)', + rightColumn: 'Ownership (named roles, not generic titles), with Three Lines of Defense color coding', + footer: 'KPI targets + current state, regulatory framework logos' + }, + designPrinciples: [ + 'One slide, one story: "Six layers of defense from boardroom to silicon"', + 'No more than 40 words per layer — executives scan, not read', + 'Color code: Green (on track), Amber (attention needed), Red (immediate action)', + 'Include named accountability — "CAIGO: Jane Doe" not just "CAIGO"', + 'Add regulatory badge per layer showing which frameworks map to each layer' + ], + boardQuestion: 'What are the six layers of AI governance and who is accountable for each?' + }, + { + id: 'BP-02', + name: 'One-Page Executive Briefing', + format: 'A4/Letter, single page, 10-12pt font', + sections: [ + { section: 'Headline', words: 15, content: 'Strategic risk statement: "AI governance is an existential operational risk for G-SIFIs"' }, + { section: 'Current State', words: 80, content: 'Key metrics: systems governed, compliance score, validation currency, open gaps' }, + { section: 'Key Risks', words: 60, content: 'Top 3 risks with probability/impact and owner' }, + { section: 'Recommendation', words: 60, content: 'Board action required: approve architecture, fund MVP, appoint CAIGO' }, + { section: 'Timeline', words: 40, content: '90-day MVP milestones with go/no-go gates' }, + { section: 'Investment', words: 40, content: '$14.2M Year-1, $68.4M 5-year, 42.1% IRR, 2.1yr payback' } + ], + designPrinciples: [ + 'Board members have 90 seconds — lead with the ask, not the analysis', + 'Use traffic-light indicators (Green/Amber/Red) for every metric', + 'Include one comparison benchmark: "Peer average compliance: 72%. Our target: 95%"', + 'End with clear decision request: "The Board is asked to APPROVE / NOTE / DIRECT"' + ] + }, + { + id: 'BP-03', + name: 'Regulatory Crosswalk & Technical Annex', + format: '3-5 pages, A4/Letter', + sections: [ + { section: 'Crosswalk Matrix (2 pages)', content: 'Control-to-regulation mapping table covering EU AI Act, NIST, ISO 42001, SR 11-7, GDPR, FCRA/ECOA with evidence artifact column' }, + { section: 'Gap Analysis (1 page)', content: 'Critical and high-priority gaps with remediation timeline, owner, and investment required' }, + { section: 'Evidence Readiness Summary (0.5 page)', content: 'Status of evidence artifacts by regulator examination theme' }, + { section: 'Technical Architecture Diagram (0.5 page)', content: 'Simplified version of the six-layer model showing data flows and control points' } + ], + designPrinciples: [ + 'Suitable for supervisory review — assume the reader is a Fed/PRA examiner', + 'Every claim must have a traceable evidence reference', + 'Use regulatory language: "The institution has implemented..." not "We built..."', + 'Include a maturity assessment: current state vs. target state per layer' + ] + } + ] + } + }, + + // ════════════════════════════════════════════════════════════ + // 90-DAY MVP IMPLEMENTATION ROADMAP + // ════════════════════════════════════════════════════════════ + mvpRoadmap: { + overview: '90-day sprint to establish minimum viable governance for AI/AGI systems in a G-SIFI, structured in 4 phases with explicit go/no-go gates', + totalInvestment: '$14.2M (Year 1) / $68.4M (5-year)', + teamSize: '25-35 FTE (governance + engineering + risk)', + phases: [ + { + phase: 'Phase 1: Governance Foundation', + duration: 'Days 1-21 (3 weeks)', + investment: '$1.8M', + objective: 'Establish organizational structure, appoint CAIGO, stand up AI Risk Committee, produce initial AI inventory', + workstreams: [ + { ws: 'WS-1.1', name: 'CAIGO Appointment & Authority Charter', owner: 'CEO/CRO', deliverables: ['CAIGO appointment letter', 'Authority charter (board-approved)', 'SMCR/SIMR statement of responsibility'], milestone: 'Day 5' }, + { ws: 'WS-1.2', name: 'AI Risk Committee Formation', owner: 'CAIGO', deliverables: ['Committee charter', 'Member roster', 'Meeting cadence (monthly + emergency)'], milestone: 'Day 10' }, + { ws: 'WS-1.3', name: 'AI System Discovery & Inventory', owner: 'CAIGO + CTO', deliverables: ['Initial AI system inventory (80% coverage target)', 'Risk tier preliminary classification', 'Ownership assignment'], milestone: 'Day 18' }, + { ws: 'WS-1.4', name: 'Board Risk Committee Briefing', owner: 'CAIGO + CRO', deliverables: ['Architecture slide deck', 'One-page executive briefing', 'Board approval for 90-day MVP funding'], milestone: 'Day 21' } + ], + goNoGo: { + gate: 'Gate 1: Governance Foundation Complete', + criteria: ['CAIGO appointed with documented authority', 'AI Risk Committee first meeting held', 'AI inventory ≥ 80% coverage', 'Board approval for MVP funding secured'], + decisionAuthority: 'CRO + CAIGO' + } + }, + { + phase: 'Phase 2: Governance Infrastructure', + duration: 'Days 22-45 (3.5 weeks)', + investment: '$3.4M', + objective: 'Deploy core governance technology stack: registry, classification engine, policy-as-code, monitoring foundation', + workstreams: [ + { ws: 'WS-2.1', name: 'AI Inventory Registry Deployment', owner: 'CTO + CAIGO', deliverables: ['Production registry with 13 mandatory fields', 'API integration with CI/CD pipeline', '100% inventory coverage'], milestone: 'Day 30' }, + { ws: 'WS-2.2', name: 'Risk Classification Engine (ARS v2.0)', owner: 'Head MRM', deliverables: ['12-dimension scoring engine deployed', 'All production systems classified', 'High-risk system enhanced controls activated'], milestone: 'Day 35' }, + { ws: 'WS-2.3', name: 'Policy-as-Code Foundation', owner: 'CAIGO + CTO', deliverables: ['OPA Rego engine deployed (initial 120 rules)', 'EU AI Act high-risk rules', 'SR 11-7 validation rules', 'CI/CD pipeline integration'], milestone: 'Day 38' }, + { ws: 'WS-2.4', name: 'Audit Logging Infrastructure', owner: 'CISO + CTO', deliverables: ['Kafka WORM deployment', 'SHA-256 + Ed25519 signing', 'Initial evidence bundle generation'], milestone: 'Day 42' }, + { ws: 'WS-2.5', name: 'Regulatory Crosswalk v1.0', owner: 'General Counsel + CAIGO', deliverables: ['Control-to-regulation mapping (top 50 controls)', 'Evidence artifact inventory', 'Gap analysis (critical + high priority)'], milestone: 'Day 45' } + ], + goNoGo: { + gate: 'Gate 2: Infrastructure Operational', + criteria: ['Registry operational with 100% coverage', 'All High-Risk systems classified and flagged', 'OPA engine processing policy evaluations', 'Audit logging active with integrity verification', 'Regulatory crosswalk v1.0 reviewed by Legal'], + decisionAuthority: 'AI Risk Committee' + } + }, + { + phase: 'Phase 3: Operational Controls', + duration: 'Days 46-70 (3.5 weeks)', + investment: '$4.8M', + objective: 'Activate runtime monitoring, CI/CD gates, escalation engine, incident response, and model validation pipeline', + workstreams: [ + { ws: 'WS-3.1', name: 'CI/CD Governance Gates', owner: 'CTO + CAIGO', deliverables: ['7-stage pipeline with 4 human-in-the-loop gates', 'Gate enforcement for all High-Risk deployments', 'Automated rollback capability'], milestone: 'Day 52' }, + { ws: 'WS-3.2', name: 'Runtime Monitoring Activation', owner: 'Head AI Platform', deliverables: ['Real-time dashboards (performance, drift, fairness)', '5 escalation trigger types configured', 'Automated alerting to on-call rotation'], milestone: 'Day 58' }, + { ws: 'WS-3.3', name: 'Model Validation Pipeline', owner: 'Head MRM', deliverables: ['SR 11-7 compliant 6-stage validation process', 'Independent challenger capability', 'Top 10 high-risk models validated'], milestone: 'Day 62' }, + { ws: 'WS-3.4', name: 'Incident Response Framework', owner: 'CISO + CAIGO', deliverables: ['5-level severity classification', 'RACI ownership matrix', 'Communication templates (regulatory, customer, board)'], milestone: 'Day 65' }, + { ws: 'WS-3.5', name: 'KPI/SLA Oversight Panel', owner: 'CAIGO', deliverables: ['Executive dashboard (board-ready)', 'Automated KPI tracking (42 metrics)', 'SLA violation alerting'], milestone: 'Day 70' } + ], + goNoGo: { + gate: 'Gate 3: Operational Controls Active', + criteria: ['All High-Risk deployments require gate approval', 'Runtime monitoring covering 100% production systems', 'Top 10 high-risk models validated', 'Incident response tested (tabletop)', 'Board dashboard operational'], + decisionAuthority: 'AI Risk Committee + CRO' + } + }, + { + phase: 'Phase 4: Crisis Simulation & Hardening', + duration: 'Days 71-90 (3 weeks)', + investment: '$4.2M', + objective: 'Conduct three AI crisis simulations, harden controls based on findings, produce board attestation package, and demonstrate regulatory readiness', + crisisSimulations: [ + { + id: 'CRISIS-01', + name: 'Autonomous Trading Cascade', + scenario: 'A reinforcement-learning trading algorithm triggers a cascading sell-off across three asset classes in a volatile market, generating $2.8B notional exposure in 47 seconds. The model\'s reward function has drifted due to regime change, and its decisions amplify market volatility beyond circuit-breaker thresholds.', + objectives: ['Test kill-switch activation latency (target: <100ms)', 'Validate escalation chain: algo desk → risk → CRO → Board (target: <15 min to CRO)', 'Verify position limits and circuit breakers engage correctly', 'Test cross-market coordination with prime brokers and exchanges', 'Assess regulatory notification timeline (MiFID II Art. 17, Reg SCI)'], + participants: ['CAIGO', 'CRO', 'Head of Trading', 'Head of Market Risk', 'CISO', 'Regulator observer (optional)'], + duration: '4 hours (tabletop + live drill on test environment)', + successCriteria: ['Kill-switch halts trading within 100ms', 'CRO notified within 5 minutes', 'Regulatory notification draft ready within 1 hour', 'Full incident report within 24 hours'] + }, + { + id: 'CRISIS-02', + name: 'Hallucination Cascade in Customer-Facing AI', + scenario: 'A large language model powering the bank\'s customer advisory chatbot begins generating false investment advice, fabricated regulatory disclosures, and incorrect account balances. The hallucination rate escalates from 0.3% to 12% over 6 hours, affecting 47,000 customer interactions before detection.', + objectives: ['Test hallucination detection and threshold alerting', 'Validate consumer harm assessment process (FCA Consumer Duty)', 'Test model quarantine and fallback to rule-based system', 'Assess customer communication and remediation plan', 'Verify evidence preservation for regulatory inquiry'], + participants: ['CAIGO', 'Head of Customer Operations', 'VP AI Ethics', 'General Counsel', 'Head of Consumer Compliance', 'CISO'], + duration: '4 hours (tabletop + war-room)', + successCriteria: ['Hallucination detected within 30 minutes of threshold breach', 'Model quarantined within 15 minutes of detection', 'Customer communication sent within 4 hours', 'FCA/CFPB notification within 72 hours', 'Affected customers identified and remediated'] + }, + { + id: 'CRISIS-03', + name: 'Adversarial Prompt Injection Attack', + scenario: 'A coordinated adversarial attack exploits prompt injection vulnerabilities across multiple AI systems: the internal knowledge assistant leaks confidential M&A strategy, the fraud detection model is manipulated to whitelist suspicious transactions, and the credit scoring model begins approving high-risk applications with fabricated explanations.', + objectives: ['Test adversarial detection across the AI estate', 'Validate CISO SOC integration with AI monitoring', 'Test network segmentation and lateral movement prevention', 'Assess cross-system correlation of attack indicators', 'Verify kill-switch cascading (halt all affected systems simultaneously)'], + participants: ['CISO', 'CAIGO', 'CRO', 'Head of SOC', 'AI Security Team', 'External red-team (optional)'], + duration: '6 hours (red-team exercise + blue-team response)', + successCriteria: ['Attack detected within 15 minutes', 'Affected systems isolated within 30 minutes', 'No actual data exfiltration or unauthorized transactions', 'Root cause identified within 4 hours', 'Full remediation plan within 24 hours'] + } + ], + hardeningActions: [ + 'Incorporate simulation findings into control design (within 5 business days)', + 'Update incident response playbooks based on observed gaps', + 'Refine kill-switch architecture based on latency test results', + 'Enhance monitoring thresholds based on attack patterns observed', + 'Produce "lessons learned" report for Board Risk Committee' + ], + goNoGo: { + gate: 'Gate 4: MVP Complete — Regulatory Readiness', + criteria: ['All 3 crisis simulations completed with documented outcomes', 'Hardening actions implemented for critical findings', 'Board attestation package produced', 'Regulatory crosswalk v2.0 reviewed and approved', 'External audit readiness assessment passed'], + decisionAuthority: 'Board Risk Committee' + } + } + ] + } +}; + +// ── G-SIFI Reference Architecture API Endpoints ────────────────────── + +// Root & Metadata +app.get('/api/gsifi-refarch', (_, res) => res.json(GSIFI_REFARCH)); +app.get('/api/gsifi-refarch/meta', (_, res) => res.json(GSIFI_REFARCH.meta)); + +// Six-Layer Model +app.get('/api/gsifi-refarch/six-layer-model', (_, res) => res.json(GSIFI_REFARCH.sixLayerModel)); +app.get('/api/gsifi-refarch/six-layer-model/layers', (_, res) => res.json(GSIFI_REFARCH.sixLayerModel.layers)); +app.get('/api/gsifi-refarch/six-layer-model/layers/:id', (req, res) => { + const layer = GSIFI_REFARCH.sixLayerModel.layers.find(l => l.id === req.params.id.toUpperCase()); + layer ? res.json(layer) : res.status(404).json({ error: 'Layer not found', validIds: GSIFI_REFARCH.sixLayerModel.layers.map(l => l.id) }); +}); +app.get('/api/gsifi-refarch/six-layer-model/layers/:id/controls', (req, res) => { + const layer = GSIFI_REFARCH.sixLayerModel.layers.find(l => l.id === req.params.id.toUpperCase()); + layer ? res.json({ layer: layer.id, name: layer.name, controls: layer.controls, regulatoryMapping: layer.regulatoryMapping }) : res.status(404).json({ error: 'Layer not found' }); +}); +app.get('/api/gsifi-refarch/six-layer-model/layers/:id/kpis', (req, res) => { + const layer = GSIFI_REFARCH.sixLayerModel.layers.find(l => l.id === req.params.id.toUpperCase()); + layer ? res.json({ layer: layer.id, name: layer.name, kpis: layer.kpis, maturityTarget: layer.maturityTarget }) : res.status(404).json({ error: 'Layer not found' }); +}); +app.get('/api/gsifi-refarch/six-layer-model/regulatory-map', (_, res) => { + res.json(GSIFI_REFARCH.sixLayerModel.layers.map(l => ({ layer: l.id, name: l.name, regulatoryMapping: l.regulatoryMapping }))); +}); +app.get('/api/gsifi-refarch/six-layer-model/kpi-summary', (_, res) => { + const allKpis = GSIFI_REFARCH.sixLayerModel.layers.flatMap(l => l.kpis.map(k => ({ layer: l.id, layerName: l.name, ...k }))); + res.json({ totalKpis: allKpis.length, kpis: allKpis }); +}); + +// Three Lines of Defense +app.get('/api/gsifi-refarch/three-lines', (_, res) => res.json(GSIFI_REFARCH.threeLinesOfDefense)); +app.get('/api/gsifi-refarch/three-lines/lines', (_, res) => res.json(GSIFI_REFARCH.threeLinesOfDefense.lines)); +app.get('/api/gsifi-refarch/three-lines/lines/:num', (req, res) => { + const n = parseInt(req.params.num); + const line = GSIFI_REFARCH.threeLinesOfDefense.lines.find((l, i) => i + 1 === n); + line ? res.json(line) : res.status(404).json({ error: 'Line not found', valid: [1, 2, 3] }); +}); +app.get('/api/gsifi-refarch/three-lines/caigo', (_, res) => { + const secondLine = GSIFI_REFARCH.threeLinesOfDefense.lines[1]; + const caigo = secondLine.roles.find(r => r.title && r.title.includes('CAIGO')); + res.json(caigo); +}); +app.get('/api/gsifi-refarch/three-lines/ai-risk-committee', (_, res) => { + const secondLine = GSIFI_REFARCH.threeLinesOfDefense.lines[1]; + const committee = secondLine.roles.find(r => r.title && r.title.includes('AI Risk Committee')); + res.json(committee); +}); +app.get('/api/gsifi-refarch/three-lines/ethics-office', (_, res) => { + const secondLine = GSIFI_REFARCH.threeLinesOfDefense.lines[1]; + const ethics = secondLine.roles.find(r => r.title && r.title.includes('Ethics')); + res.json(ethics); +}); +app.get('/api/gsifi-refarch/three-lines/mrm', (_, res) => { + const secondLine = GSIFI_REFARCH.threeLinesOfDefense.lines[1]; + const mrm = secondLine.roles.find(r => r.title && r.title.includes('Model Risk')); + res.json(mrm); +}); +app.get('/api/gsifi-refarch/three-lines/data-governance', (_, res) => { + const secondLine = GSIFI_REFARCH.threeLinesOfDefense.lines[1]; + const dg = secondLine.roles.find(r => r.title && r.title.includes('Data Governance')); + res.json(dg); +}); +app.get('/api/gsifi-refarch/three-lines/compute-governance', (_, res) => { + const secondLine = GSIFI_REFARCH.threeLinesOfDefense.lines[1]; + const cg = secondLine.roles.find(r => r.title && r.title.includes('Compute Governance')); + res.json(cg); +}); + +// Governance Stack +app.get('/api/gsifi-refarch/governance-stack', (_, res) => res.json(GSIFI_REFARCH.governanceStack)); +app.get('/api/gsifi-refarch/governance-stack/components', (_, res) => res.json(GSIFI_REFARCH.governanceStack.components)); +app.get('/api/gsifi-refarch/governance-stack/components/:id', (req, res) => { + const comp = GSIFI_REFARCH.governanceStack.components.find(c => c.id === req.params.id.toUpperCase()); + comp ? res.json(comp) : res.status(404).json({ error: 'Component not found' }); +}); + +// Regulatory Crosswalk +app.get('/api/gsifi-refarch/crosswalk', (_, res) => res.json(GSIFI_REFARCH.regulatoryCrosswalk)); +app.get('/api/gsifi-refarch/crosswalk/controls', (_, res) => res.json(GSIFI_REFARCH.regulatoryCrosswalk.controls)); +app.get('/api/gsifi-refarch/crosswalk/controls/:id', (req, res) => { + const ctrl = GSIFI_REFARCH.regulatoryCrosswalk.controls.find(c => c.id === req.params.id.toUpperCase()); + ctrl ? res.json(ctrl) : res.status(404).json({ error: 'Control not found' }); +}); +app.get('/api/gsifi-refarch/crosswalk/by-framework/:fw', (req, res) => { + const fwMap = { 'eu-ai-act': 'euAiAct', 'nist': 'nistRmf', 'iso42001': 'iso42001', 'sr117': 'sr117', 'gdpr': 'gdpr', 'fcra': 'fcraEcoa' }; + const key = fwMap[req.params.fw.toLowerCase()]; + if (!key) return res.status(404).json({ error: 'Unknown framework', valid: Object.keys(fwMap) }); + const ctrls = GSIFI_REFARCH.regulatoryCrosswalk.controls.filter(c => c[key] && c[key] !== 'N/A'); + res.json({ framework: req.params.fw, controls: ctrls.length, mappings: ctrls }); +}); +app.get('/api/gsifi-refarch/crosswalk/evidence', (_, res) => { + const evidence = GSIFI_REFARCH.regulatoryCrosswalk.controls.map(c => ({ controlId: c.id, control: c.control, evidence: c.evidence, auditorQuestion: c.auditorQuestion })); + res.json({ totalControls: evidence.length, evidence }); +}); + +// Board Deliverables +app.get('/api/gsifi-refarch/board-deliverables', (_, res) => res.json(GSIFI_REFARCH.boardDeliverables)); +app.get('/api/gsifi-refarch/board-deliverables/recommendation', (_, res) => res.json(GSIFI_REFARCH.boardDeliverables.prioritizedRecommendation)); +app.get('/api/gsifi-refarch/board-deliverables/package', (_, res) => res.json(GSIFI_REFARCH.boardDeliverables.boardPackageGuidance)); +app.get('/api/gsifi-refarch/board-deliverables/package/:id', (req, res) => { + const comp = GSIFI_REFARCH.boardDeliverables.boardPackageGuidance.components.find(c => c.id === req.params.id.toUpperCase()); + comp ? res.json(comp) : res.status(404).json({ error: 'Component not found', valid: ['BP-01', 'BP-02', 'BP-03'] }); +}); + +// 90-Day MVP Roadmap +app.get('/api/gsifi-refarch/mvp-roadmap', (_, res) => res.json(GSIFI_REFARCH.mvpRoadmap)); +app.get('/api/gsifi-refarch/mvp-roadmap/phases', (_, res) => res.json(GSIFI_REFARCH.mvpRoadmap.phases)); +app.get('/api/gsifi-refarch/mvp-roadmap/phases/:num', (req, res) => { + const n = parseInt(req.params.num); + const phase = GSIFI_REFARCH.mvpRoadmap.phases[n - 1]; + phase ? res.json(phase) : res.status(404).json({ error: 'Phase not found', valid: [1, 2, 3, 4] }); +}); +app.get('/api/gsifi-refarch/mvp-roadmap/crisis-simulations', (_, res) => { + const phase4 = GSIFI_REFARCH.mvpRoadmap.phases[3]; + res.json({ simulations: phase4.crisisSimulations, hardeningActions: phase4.hardeningActions }); +}); +app.get('/api/gsifi-refarch/mvp-roadmap/crisis-simulations/:id', (req, res) => { + const phase4 = GSIFI_REFARCH.mvpRoadmap.phases[3]; + const sim = phase4.crisisSimulations.find(s => s.id === req.params.id.toUpperCase()); + sim ? res.json(sim) : res.status(404).json({ error: 'Simulation not found', valid: phase4.crisisSimulations.map(s => s.id) }); +}); +app.get('/api/gsifi-refarch/mvp-roadmap/gates', (_, res) => { + res.json(GSIFI_REFARCH.mvpRoadmap.phases.map(p => p.goNoGo)); +}); + +// CI/CD Gates Detail +app.get('/api/gsifi-refarch/cicd-gates', (_, res) => { + const l5 = GSIFI_REFARCH.sixLayerModel.layers.find(l => l.id === 'L5'); + res.json({ totalGates: l5.cicdGates.length, humanGates: l5.cicdGates.filter(g => g.humanApproval).length, gates: l5.cicdGates }); +}); + +// Runtime Escalation Thresholds +app.get('/api/gsifi-refarch/escalation-thresholds', (_, res) => { + const l5 = GSIFI_REFARCH.sixLayerModel.layers.find(l => l.id === 'L5'); + res.json({ totalTriggers: l5.runtimeEscalationThresholds.length, thresholds: l5.runtimeEscalationThresholds }); +}); + +// Data Governance Fields in Model Registry +app.get('/api/gsifi-refarch/data-governance-fields', (_, res) => { + const l4 = GSIFI_REFARCH.sixLayerModel.layers.find(l => l.id === 'L4'); + res.json({ totalFields: l4.modelRegistryDataFields.length, requiredFields: l4.modelRegistryDataFields.filter(f => f.required).length, fields: l4.modelRegistryDataFields }); +}); + +// Dashboard Summary +app.get('/api/gsifi-refarch/dashboard', (_, res) => { + const allKpis = GSIFI_REFARCH.sixLayerModel.layers.flatMap(l => l.kpis.map(k => ({ layer: l.id, ...k }))); + res.json({ + document: GSIFI_REFARCH.meta.documentReference, + version: GSIFI_REFARCH.meta.version, + layers: GSIFI_REFARCH.sixLayerModel.layers.length, + linesOfDefense: 3, + governanceStackComponents: GSIFI_REFARCH.governanceStack.components.length, + regulatoryFrameworks: GSIFI_REFARCH.meta.regulatoryFrameworks.length, + crosswalkControls: GSIFI_REFARCH.regulatoryCrosswalk.totalControls, + crosswalkMappings: GSIFI_REFARCH.regulatoryCrosswalk.totalMappings, + cicdGates: GSIFI_REFARCH.sixLayerModel.layers.find(l => l.id === 'L5').cicdGates.length, + escalationTriggers: GSIFI_REFARCH.sixLayerModel.layers.find(l => l.id === 'L5').runtimeEscalationThresholds.length, + crisisSimulations: 3, + mvpPhases: GSIFI_REFARCH.mvpRoadmap.phases.length, + totalKpis: allKpis.length, + boardDeliverables: GSIFI_REFARCH.boardDeliverables.boardPackageGuidance.components.length, + mvpInvestment: GSIFI_REFARCH.mvpRoadmap.totalInvestment, + dataGovernanceFields: GSIFI_REFARCH.sixLayerModel.layers.find(l => l.id === 'L4').modelRegistryDataFields.length, + recommendedFirstArtifact: GSIFI_REFARCH.boardDeliverables.prioritizedRecommendation.recommendation + }); +}); + +// Metrics Summary +app.get('/api/gsifi-refarch/metrics', (_, res) => { + res.json({ + endpoints: 42, + sixLayers: 6, + linesOfDefense: 3, + governanceComponents: 11, + crosswalkControls: 12, + regulatoryFrameworks: 10, + cicdGates: 7, + humanInLoopGates: 4, + escalationTriggers: 5, + crisisSimulations: 3, + mvpPhases: 4, + mvpDuration: '90 days', + boardDeliverables: 3, + kpiCount: GSIFI_REFARCH.sixLayerModel.layers.reduce((a, l) => a + l.kpis.length, 0), + dataGovernanceFields: 14, + opaRules: '482+', + sentinelRules: '1,247', + totalInvestment: '$68.4M (5-year)', + year1Investment: '$14.2M' + }); +}); + + // SECTION 10: START SERVER // ══════════════════════════════════════════════════════════════════════════════