Preserve failures through governance log pipelines

Author: OneFineStarstuff

.github/workflows/gsifi-governance-artifacts.yml

@@ -12,6 +12,13 @@ on:
       - 'docs/reports/GSIFI_AGI_ASI_GOVERNANCE_BLUEPRINT_2026_2030.md'
       - 'docs/reports/GSIFI_GOVERNANCE_ARTIFACTS_RUNBOOK.md'
       - 'docs/reports/.markdownlint.json'
+      - 'governance_blueprint/**/*.json'
+      - 'governance_blueprint/**/*.yaml'
+      - 'governance_blueprint/**/*.toml'
+      - 'governance_blueprint/**/*.rego'
+      - 'governance_blueprint/**/*.bif'
+      - 'governance_blueprint/**/*.csv'
+      - 'governance_blueprint/validation/**/*.py'
       - 'scripts/validate_gsifi_governance_assets.py'
       - 'scripts/__init__.py'
       - 'tests/**/*.py'
@@ -53,11 +60,14 @@ jobs:
           python -m pip install --upgrade pip
           pip install -e .[governance] pytest
 
-      - name: Verify installed console entrypoint
-        run: validate-gsifi-governance-assets --help
+      - name: Run consolidated governance CI target
+        run: make gov-all
 
-      - name: Run installed console entrypoint against default artifacts
-        run: validate-gsifi-governance-assets
-
-      - name: Run governance check suite
-        run: make check-gsifi-governance
+      - name: Upload governance validation reports
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: governance-validation-reports
+          path: artifacts/governance/
+          if-no-files-found: error
+          retention-days: 30

.gitignore

@@ -40,3 +40,6 @@ __pycache__/
 
 # Governance test artifacts
 artifacts/test-results/
+artifacts/governance/
+governance-artifact-validation-report.json
+governance-validation-suite-report.json

ENTERPRISE_AGI_ASI_GOVERNANCE_BLUEPRINT_2026_2030.md

@@ -421,7 +421,143 @@ Track by capability value stream rather than only cost center:
 
 ---
 
-## 12) Regulator engagement and assurance playbook
+## 12) Regulatory deep-integration profile for G-SIFIs
+
+### 12.1 Mandatory cross-framework traceability requirements
+
+For Tier 3/4 systems, require machine-traceable linkage between:
+
+- **EU AI Act Annex IV technical documentation** fields and internal model cards.
+- **NIST AI RMF 1.0 + NIST AI 600-1** controls and test evidence.
+- **ISO/IEC 42001** AIMS clauses and internal audit controls.
+- **OECD AI Principles** and conduct/fairness controls.
+- **GDPR Article 22** automated-decision safeguards and human-review workflows.
+- **FCRA/ECOA** adverse action and explainability obligations.
+- **Basel III/IV + SR 11-7** model risk governance/validation and capital-materiality overlays.
+- **NIS2** cyber resilience and incident management controls.
+- **FCA Consumer Duty/SMCR** accountability outcomes and Senior Manager attestations.
+- **MAS/HKMA FEAT** fairness, ethics, accountability, transparency control mappings.
+- **US Executive Order 14110** historical control lineage (for policy continuity and evidentiary rationale).
+
+### 12.2 Control stack reference implementation
+
+- **Sentinel AI Governance Platform v2.4** as central policy/evidence control plane.
+- **WorkflowAI Pro Agent Lifecycle Management** for approvals, HITL routing, and agent decommissioning.
+- **EAIP** for policy-enforced model/tool mediation.
+- **High-assurance RAG** with source allowlists, provenance scoring, and retrieval-policy gates.
+- **Kubernetes/Kafka/OPA zero-trust stack** as primary runtime; hardened **Docker Swarm** accepted for legacy estates under compensating controls.
+- **Node.js/Python governance sidecars** for runtime attestations, policy telemetry, and control heartbeat signals.
+- **Next.js explainability frontends** for customer/regulator narratives and adverse-outcome reason trails.
+- **Terraform/CI/CD governance automation** with OPA/Rego release gates and separation-of-duty checks.
+- **Kafka WORM audit logging**, **PQC signature envelopes**, and **zk-SNARK-based access proofs** for privacy-preserving evidence access.
+
+### 12.3 Advanced technical standards
+
+- Hyperparameter control standards:
+  - Approved parameter bands by model family and risk tier.
+  - Dual approval for sensitivity-impacting changes.
+  - Automatic rollback on out-of-band changes.
+- Drift standards:
+  - Statistical + semantic drift thresholds.
+  - Tier-dependent revalidation SLAs.
+  - Mandatory challenger invocation when drift persists.
+- Deterministic audit replay:
+  - Snapshot pinning of prompts, model weights/version IDs, retrieval corpus hashes, policy bundles, and feature data versions.
+  - Time-indexed replay harness to reconstruct decision pathways for legal/supervisory review.
+- Adversarial red teaming:
+  - Scenario libraries for fraud, AML evasion, market manipulation, data leakage, jailbreak, and autonomous tool misuse.
+  - Independent red-team challenge cycles for all Tier 4 and frontier systems.
+- Cognitive Resonance monitoring:
+  - Detect coherence-shift anomalies, latent goal drift, and policy-inconsistent reasoning trajectories.
+  - Trigger containment escalation tiers automatically.
+
+---
+
+## 13) AGI/ASI containment and systemic risk blueprint
+
+### 13.1 Containment laboratories and assurance protocols
+
+- Establish AGI containment labs with:
+  - Air-gapped evaluation zones where feasible.
+  - Controlled external interfaces with protocol-level allowlists.
+  - Continuous tripwire instrumentation.
+- Deploy **CAS-SPP** containment assurance workflows:
+  - Capability Assessment Scoring (CAS).
+  - Safety Proof Packaging (SPP) for executive/regulator review.
+- Use **Bayesian Belief Networks** for dynamic systemic risk propagation estimates across markets, payment rails, liquidity, and cyber dependencies.
+
+### 13.2 Safety framework alignment
+
+- Operationalize **Luminous Engine Codex** as high-assurance policy/engineering codification for frontier model operations.
+- Implement **Cognitive Resonance Protocol** as a standardized monitoring and intervention pipeline.
+- Integrate **Sentinel/Omni-Sentinel** supervisory overlays for:
+  - Multi-entity risk heatmaps.
+  - Cross-jurisdiction containment readiness.
+  - Coordinated crisis command views.
+
+### 13.3 Crisis simulation and frontier taxonomy program
+
+- Quarterly systemic crisis simulations:
+  - AI-driven bank-run amplification.
+  - Payment-network disruption by autonomous agents.
+  - Coordinated synthetic identity/fraud campaigns.
+  - Large-scale misinformation and market sentiment distortion.
+- Frontier risk taxonomy (minimum domains):
+  - Autonomous replication/self-improvement risk.
+  - Strategic deception and hidden-objective risk.
+  - Economic concentration/compute chokepoint risk.
+  - Critical infrastructure manipulation risk.
+  - Governance evasion and oversight bypass risk.
+
+---
+
+## 14) Civilizational-scale AI and compute governance interface
+
+### 14.1 International compute governance operating model
+
+- Participate in **International Compute Governance Consortium (ICGC)** for shared norms and verification tooling.
+- Support global compute registries with:
+  - Trusted accelerator inventory declarations.
+  - Workload purpose-binding attestations.
+  - Cross-border compute transfer notification workflows.
+
+### 14.2 Treaty-aligned mechanisms (enterprise integration checklist)
+
+Map enterprise controls to treaty-aligned systemic mechanisms:
+
+- **GACRA** (Global AI Crisis Response Accord)
+- **GASO** (Global AI Safety Observatory)
+- **GFMCF** (Global Frontier Model Compute Framework)
+- **GAICS** (Global AI Incident Classification Standard)
+- **GAIVS** (Global AI Verification Scheme)
+- **GACP** (Global AI Compute Passporting)
+- **GATI** (Global AI Transparency Interchange)
+- **GACMO** (Global AI Change Management Observatory)
+- **FTEWS** (Frontier Threat Early Warning System)
+- **GAI-SOC** (Global AI Security Operations Coalition)
+- **GAIGA** (Global AI Governance Assurance)
+- **GACRLS** (Global AI Compute Risk Ledger Standard)
+- **GFCO** (Global Frontier Compute Oversight)
+- **GAID** (Global AI Incident Disclosure)
+- **GASCF** (Global AI Systemic Containment Framework)
+
+### 14.3 Machine-readable governance artifact set
+
+Minimum artifacts for board, C-suite, regulators, enterprise architects, platform engineers, and AI safety researchers:
+
+- `governance_blueprint/systemic_artifacts/ai_system_registry.yaml` — system inventory, owners, tiering, jurisdiction tags.
+- `governance_blueprint/systemic_artifacts/control_crosswalk.json` — legal/standard control mappings and evidence URIs.
+- `governance_blueprint/systemic_artifacts/agent_lifecycle_policy.rego` — agent runtime/approval/decommission policies.
+- `governance_blueprint/systemic_artifacts/containment_safety_case.jsonld` — safety claims, evidence, and approval signatures.
+- `governance_blueprint/systemic_artifacts/systemic_risk_bbn_model.bif` — Bayesian network structure and priors.
+- `governance_blueprint/systemic_artifacts/crisis_simulation_catalog.yaml` — scenario definitions and success/failure criteria.
+- `governance_blueprint/systemic_artifacts/deterministic_replay_manifest.json` — replay dependencies and integrity hashes.
+- `governance_blueprint/systemic_artifacts/regulator_submission_bundle.toml` — jurisdiction-specific reporting package index.
+
+---
+---
+
+## 15) Regulator engagement and assurance playbook
 
 1. **Supervisory narrative**: explain governance design, risk appetite, accountability chain.
 2. **Evidence walk-through**: show immutable logs, approvals, validation artifacts, issue remediation.
@@ -433,7 +569,7 @@ Prepare jurisdiction-specific annexes (EU, US, UK, SG, HK) with local citations
 
 ---
 
-## 13) 12-month implementation checklist (quick start)
+## 16) 12-month implementation checklist (quick start)
 
 - Approve enterprise AI risk appetite and governance charter.
 - Complete AI inventory, tiering, and criticality mapping.
@@ -448,7 +584,7 @@ Prepare jurisdiction-specific annexes (EU, US, UK, SG, HK) with local citations
 
 ---
 
-## 14) Reference implementation principles (non-negotiables)
+## 17) Reference implementation principles (non-negotiables)
 
 1. **No high-risk AI in production without independent validation.**
 2. **No model change without traceable approval and rollback path.**
@@ -458,15 +594,15 @@ Prepare jurisdiction-specific annexes (EU, US, UK, SG, HK) with local citations
 
 ---
 
-## 15) Concluding guidance
+## 18) Concluding guidance
 
 Treat AI governance as an **operating system**, not a policy document. The institutions that succeed from 2026–2030 will unify legal interpretation, engineering controls, model risk discipline, and safety science into a single execution fabric with provable evidence.
 
 This blueprint is intentionally implementation-oriented: if adopted with disciplined change management, it enables both supervisory confidence and faster, safer AI scale.
 
 ---
 
-## 16) Regulator-ready control mapping matrix (starter)
+## 19) Regulator-ready control mapping matrix (starter)
 
 | Control Family | Example Internal Control ID | EU AI Act | NIST AI RMF | ISO/IEC 42001 | FS Regulatory Anchor | Evidence Artifact |
 |---|---|---|---|---|---|---|
@@ -482,9 +618,9 @@ This blueprint is intentionally implementation-oriented: if adopted with discipl
 
 ---
 
-## 17) Reference technical implementation patterns
+## 20) Reference technical implementation patterns
 
-### 17.1 Kafka + WORM evidence pipeline (minimum secure configuration)
+### 20.1 Kafka + WORM evidence pipeline (minimum secure configuration)
 
 - Dedicated cluster or logically isolated tenant for governance logs.
 - Topic strategy:
@@ -502,7 +638,7 @@ This blueprint is intentionally implementation-oriented: if adopted with discipl
   - Daily Merkle root of topic offsets + payload hashes.
   - Signed digest escrow and periodic export to WORM object store.
 
-### 17.2 OPA compliance-as-code gate example (policy intent)
+### 20.2 OPA compliance-as-code gate example (policy intent)
 
 ```rego
 package aigov.release
@@ -525,7 +661,7 @@ allow {
 }
 ```
 
-### 17.3 Governance sidecar contract (Node.js/Python services)
+### 20.3 Governance sidecar contract (Node.js/Python services)
 
 Each AI-serving workload should emit a normalized evidence envelope:
 
@@ -536,7 +672,7 @@ Each AI-serving workload should emit a normalized evidence envelope:
 - `latency_ms`, `confidence`, `safety_filter_events`
 - `trace_id`, `request_id`, `jurisdiction_code`, `timestamp_utc`
 
-### 17.4 Terraform and CI/CD governance controls
+### 20.4 Terraform and CI/CD governance controls
 
 - Enforce policy checks in plan/apply pipelines (deny drift from approved baseline tags).
 - Require signed module versions from trusted registries.
@@ -545,9 +681,9 @@ Each AI-serving workload should emit a normalized evidence envelope:
 
 ---
 
-## 18) Financial services scenario packs (implementation detail)
+## 21) Financial services scenario packs (implementation detail)
 
-### 18.1 Credit underwriting scenario pack
+### 21.1 Credit underwriting scenario pack
 
 - Pre-decision checks:
   - data recency and completeness controls,
@@ -562,24 +698,24 @@ Each AI-serving workload should emit a normalized evidence envelope:
   - adverse impact trend analysis,
   - customer complaint correlation analysis.
 
-### 18.2 Fraud/AML scenario pack
+### 21.2 Fraud/AML scenario pack
 
 - Alert model transparency scorecards.
 - Analyst feedback loop to reduce false positives and detect automation bias.
 - Rule-model hybrid fallback when model confidence degrades.
 - Governance on suspicious activity narrative generation (factuality controls).
 
-### 18.3 Treasury/market risk support scenario pack
+### 21.3 Treasury/market risk support scenario pack
 
 - Stress and reverse-stress testing for forecasting AI.
 - Hard limits: AI recommendations cannot auto-execute high-impact market actions without human authorization.
 - Real-time anomaly monitors for regime shifts.
 
 ---
 
-## 19) AGI/ASI readiness protocol (enterprise safety case template)
+## 22) AGI/ASI readiness protocol (enterprise safety case template)
 
-### 19.1 Safety case minimum sections
+### 22.1 Safety case minimum sections
 
 1. System boundary and intended capability envelope.
 2. Hazard analysis and misuse threat model.
@@ -588,7 +724,7 @@ Each AI-serving workload should emit a normalized evidence envelope:
 5. Monitoring triggers and rollback/kill criteria.
 6. External review summary (for Tier 4/C4+ systems).
 
-### 19.2 Escalation triggers for potential frontier discontinuity
+### 22.2 Escalation triggers for potential frontier discontinuity
 
 Escalate immediately to executive crisis governance when any of the following are observed:
 
@@ -599,7 +735,7 @@ Escalate immediately to executive crisis governance when any of the following ar
 
 ---
 
-## 20) Jurisdictional annex structure (for legal/compliance teams)
+## 23) Jurisdictional annex structure (for legal/compliance teams)
 
 Create annexes per operating region using a common template:
 
@@ -618,15 +754,15 @@ Each annex should include:
 
 ---
 
-## 21) Implementation PMO structure and milestone governance
+## 24) Implementation PMO structure and milestone governance
 
-### 21.1 Program governance cadence
+### 24.1 Program governance cadence
 
 - Weekly control implementation stand-up (engineering + risk + compliance).
 - Monthly AI Governance Council deep-dive (exceptions and KPI/KRI movement).
 - Quarterly Board reporting and risk appetite reaffirmation.
 
-### 21.2 Milestone quality gates
+### 24.2 Milestone quality gates
 
 - **Gate A (Design):** controls mapped, RACI complete, architecture approved.
 - **Gate B (Build):** policy-as-code tests pass, evidence pipeline active, docs complete.
@@ -635,7 +771,7 @@ Each annex should include:
 
 ---
 
-## 22) Deliverables checklist for first supervisory review cycle
+## 25) Deliverables checklist for first supervisory review cycle
 
 - Enterprise AI policy suite (approved and version-controlled).
 - Complete AI inventory with risk tiering rationale.
@@ -650,7 +786,7 @@ This package should be deliverable within 48–72 hours under supervisory reques
 
 ---
 
-## 23) Companion implementation artifacts (machine-readable)
+## 26) Companion implementation artifacts (machine-readable)
 
 To accelerate execution and reduce ambiguity, this blueprint includes machine-readable implementation assets:
 
@@ -664,7 +800,7 @@ These artifacts are intended to be adapted into enterprise repositories and inte
 
 ---
 
-## 24) Validation and CI readiness for companion artifacts
+## 27) Validation and CI readiness for companion artifacts
 
 To prevent documentation drift and ensure governance artifacts remain deployment-ready, include an automated static validation step in CI:
 
@@ -688,7 +824,7 @@ For validator quality assurance, run:
 python3 governance_blueprint/validation/selftest_validate_artifacts.py
 ```
 
-For CI enforcement, wire these checks into `.github/workflows/governance-artifacts-ci.yml` (or equivalent enterprise pipeline controls).
+For CI enforcement, wire these checks into `.github/workflows/governance-artifacts-ci.yml` and/or `.github/workflows/gsifi-governance-artifacts.yml` (or equivalent enterprise pipeline controls).
 
 For manifest integrity lifecycle management, generate/check hashes with: