Module 01 / 07 · Sovereign Risk Infrastructure
ARIN
Autonomous Risk Intelligence Network
Status
Pre-Client Institutional Diligence-Ready
Live production endpoint · arin.saa-alliance.com · SHA-256 chain · 7-yr retention
Evidence
Specialist
Agent Council
Mathematical
Validation
Meta-Decision
Governor
Red Team
Audit Trail
Trusted Verdict
S4 · System overview · Council paradigm · 22 Agents · Hydra Ontology
The multi-agent AI decision engine at the core of the Sovereign Risk Infrastructure.
ARIN is the multi-agent AI decision engine at the core of the SAA Sovereign Risk Infrastructure — a council of 22 specialist agents grounded in a living knowledge graph, governed by a Meta-Decision Governor, and continuously calibrated against realised outcomes. Routes sub-second risk math through the ARIN22 deterministic kernel (US-registered name) — kernel correctness CPU-bound: ~99 µs single kernel call, constant from D = 4 synthetic to D = 200 on real portfolios with no degradation; GPU layered for batch scale. No GPU lock-in.
Unlike single-model architectures, ARIN implements a council paradigm: 22 specialised risk agents — each with distinct analytical mandates, independent reasoning chains, and domain-specific calibration — process every entity in parallel and submit individual assessments to the Meta-Decision Governor. Agents reason against the Hydra Ontology, a living knowledge graph of 114 M nodes, 336 M relationships, and 9.1 M RAG chunks across 12 asset classes (hash-pinned audit graph), plus ~500 M operational embedding for runtime H100 similarity routing (named separately, not part of the audit substrate), organised into 7 self-evolving layers.
The Governor aggregates agent outputs using a calibration-aware, confidence-and-reliability-weighted consensus mechanism, tracks dissent patterns, and produces a single explainable verdict (BUY / SELL / HOLD / AVOID) with composite confidence, full dissent map, and regulatory audit trail. Entity Memory maintains persistent context across sessions — trend direction, anomaly flags, score deltas, recent-trend slopes — enabling longitudinal analysis and drift detection over 5,000+ calibrated verdicts with 14 curated crisis cascade patterns. The Learning Agent continuously recalibrates agent weights against realised outcomes (7d / 30d tracking windows). The exact aggregation formula is internal.
Inference is sovereignty-grade: 5 LLM providers with automatic fallback — vLLM (local H100) → NVIDIA NIM → OpenAI → DeepSeek → Ollama — eliminating single-vendor exposure. Hardware: 8× NVIDIA H100 80 GiB (DGX-class), 1.66 s mean inference latency, 1M-token KV cache for long-context retention. All LLM responses pass through NVIDIA NeMo Guardrails for compliance, safety and factuality filtering, then through a 7-rule math-to-narrative audit engine that blocks any narrative inconsistent with the underlying deterministic state. Graph analysis runs over the Hydra Ontology with a graph-neural-network layer for systemic-risk detection, dependency mapping and cascade-effect simulation. When sub-second risk math is required, ARIN delegates to the ARIN22 deterministic kernel (US-registered name) — class-routed deterministic kernel, CRN-anchored at the 99.9 tail, MC-challenger-backed on hard regimes, no silent degradation. Enterprise Wave canonical: 8,800 cases/backend · 8.8 B paths/backend · 0 execution failures; trading-desk disclosed bands — tick replay p99 0.44–0.46 ms, pre-trade gate p99 0.85–0.93 ms, pre-trade gate p999 1.8–2.5 ms. CPU-bound kernel correctness: ~99 µs single kernel call, constant from D = 4 synthetic to D = 200 on real portfolios with no degradation, orders of magnitude faster than Monte Carlo equivalent (precise × under NDA), better than 0.05% deviation at q99.9 vs MC gold-standard (vs-MC accuracy lane, separate axis from GPU/CPU parity). GPU/CPU parity 99.9 tail bands: CVaR99.9 p95 0.27–0.28%, max 0.59–0.66% — disclosed honestly. Not STAC-certified (STAC-A2/M3-inspired internal workloads only, not wire-to-wire tick-to-trade); production listed-option pricing remains fail-closed pending external market-data validation. No GPU lock-in. Detailed benchmark numerics under NDA.
S5 · Agent roster · 22 specialist units · domain-bounded · calibration-weighted
22 specialist units. Domain-bounded. Calibration-weighted.
Default probability, credit spreads, rating migration, ML-based scoring.
VaR / CVaR, volatility regime, factor exposure, drawdown analysis.
Process failure, fraud detection, business continuity, key-person risk.
Bid-ask spreads, redemption pressure, cash-flow coverage, funding stress.
Compliance exposure, sanction screening, OFAC / SDN, regulatory-change impact.
Graph-based contagion mapping, cascade simulation, interconnectedness scoring.
Sentiment extraction, narrative drift, disinformation detection, media pressure.
Exposure concentration, counterparty creditworthiness, netting agreements.
Validation of analytical assumptions, model-drift detection, backtest integrity.
Environmental exposure, social governance scoring, greenwashing detection.
Climate hazard exposure, infrastructure vulnerability, natural catastrophe.
Real-time alert monitoring, anomaly detection, threshold breach, early warning.
Ethical compliance, bias detection, reputational risk, responsible-AI audit.
Sovereign instability, trade-war impact, sanctions chain analysis, conflict zones.
CISA KEV monitoring, attack-surface analysis, data-breach probability, CVE tracking.
GDP / inflation forecasting, yield-curve analysis, central-bank policy impact.
Logistics disruption, supplier concentration, commodity dependency mapping.
On-chain analytics, DeFi protocol exposure, smart-contract risk, whale tracking.
Coverage adequacy, loss-ratio analysis, reinsurance exposure, catastrophe bonds.
Portfolio concentration, sector overweight, geographic correlation, HHI index.
Pending litigation exposure, regulatory penalties, class-action tracking, IP disputes.
Black-swan scenarios, extreme value theory, multi-factor stress testing, correlation breakdown.
S6 · Meta-Decision architecture · Governor · Memory · Outcome Loop · RAG · Learning · Historical
Six engines behind the verdict.
Meta-Decision Governor
Aggregates the 22 agent assessments using a calibration-aware, confidence-and-reliability-weighted consensus. Implements domain-bounded routing for domain-specific queries, a Zero-Outcome Freeze for insufficient data, and a Dual-Publish protocol for contested verdicts. Produces the final verdict (BUY / SELL / HOLD / AVOID) with composite confidence and full dissent map. Specific weighting and thresholds are internal.
Entity Memory
Persistent per-entity analysis store tracking every risk assessment across sessions. Before each new analysis, agents receive historical context: trend direction, statistical anomaly flags, score delta vs. previous, and a recent-trend slope. After each verdict, the analysis snapshot is saved with key factors and reasoning summary — enabling longitudinal drift detection before agents even begin their assessment.
Outcome Feedback Loop
Every verdict is pre-recorded with predicted score and direction. When actual outcomes arrive through the private outcome-ingestion contract, an outcome-scoring stage triggers post-hoc calibration of agent weights and updates the consensus parameters. An adversarial-validation layer blocks verdicts whose estimated error probability is excessive. With outcomes: a self-calibrating system. Recalibration cadence is internal.
RAG Knowledge Corpus
A curated regulatory and domain knowledge corpus is auto-injected into agent context before assessment, covering Financial Regulation (Basel III, Solvency II, TCFD / ISSB S2, IFRS 9), Climate Science (IPCC AR6, NGFS scenarios, physical-risk AAL / PML), Geopolitical, Cyber Security (NIST CSF, MITRE ATT&CK), Macro Economics, and Supply Chain. Domain auto-matched to object type.
Learning Agent
Continuously monitors verdict accuracy against realised outcomes and adjusts agent-reliability weights through industry-standard post-hoc calibration techniques. Computes calibration-error metrics to detect overconfidence. An adversarial-validation layer challenges high-error verdicts based on dissent strength, error probability and reliability, identifies degrading agents and triggers automatic weight recalibration.
Historical Events Engine
A large curated historical-event corpus across financial crises, conflicts, climate events, pandemics and cyber incidents feeds the agents’ stress-test context. Each entry carries severity, financial loss, casualty estimates, cascade effects, lessons learned and recovery timeline — so the council reasons against documented precedent rather than abstract priors.
S7 · Closed-loop architecture · Memory → Knowledge → Agents → Consensus → Outcome → Memory
Six stages. One loop. No service is isolated.
Every assessment flows through a 6-stage closed loop. No service is isolated — data cascades from memory to knowledge to agents to consensus to feedback and back to memory.
① Load → ② Enrich
① Entity Memory Load: before assessment, prior analyses for the entity are loaded — trend direction, anomaly flags, historical scores. ② Knowledge Injection: the RAG corpus auto-selects the relevant domain context (regulatory, climate, geopolitical, etc.) and injects it into agent prompts. Agents start every analysis with full institutional memory.
③ Assess → ④ Consensus
③ Agent Assessments: 22 agents run in parallel with enriched context, each seeing entity history plus domain knowledge. ④ Consensus Engine: calibration-aware, confidence-and-reliability-weighted aggregation with dissent detection, adversarial validation and adaptive coupling from outcome feedback. Produces a Decision Object with full provenance chain.
⑤ Record → ⑥ Save
⑤ Outcome Pre-Record: the verdict is persisted with its prediction. When actual results arrive, the calibration loop updates agent weighting. ⑥ Entity Memory Save: the current analysis is persisted with delta, trend and key factors. The next assessment cycle starts at ① with accumulated intelligence.
S8 · Consensus mechanics · multi-layered protocol · adversarially robust
Not simple majority voting. A multi-layered, adversarially-robust consensus protocol.
ARIN does not use simple majority voting. The Governor employs a multi-layered consensus protocol designed for adversarial robustness:
- Domain-Bounded Routing — domain-specific queries activate the relevant subset of agents; irrelevant agents are excluded from the voting pool to prevent noise dilution. Routing tables and domain-to-agent mappings are internal.
- Confidence-and-Reliability Weighting — each agent’s vote is weighted by its self-reported confidence for the current query and by its historical reliability score maintained by the Learning Agent. High-confidence and high-reliability votes dominate the consensus; the exact weighting formula is internal.
- Zero-Outcome Freeze — if no agent achieves confidence above the configured threshold, the Governor refuses to issue a verdict and flags the entity for manual review. This prevents false certainty in data-scarce scenarios.
- Dual-Publish Protocol — when the council is deeply split, the Governor publishes both majority and minority verdicts with separate confidence scores, explicit reasoning chains and a «CONTESTED» flag. No forced resolution of genuine analytical disagreement.
- Dissent Mapping — every verdict includes a full dissent map showing which agents agreed, disagreed and abstained, with individual reasoning summaries. Enables regulatory audit and explainability requirements (EU AI Act, SEC, MiFID II).
- Math-to-Narrative Audit Engine — before publication, every narrative explanation is verified against the deterministic mathematical state. Seven structural rules (below) block hallucination at the publication gate. A narrative cannot be released if it disagrees with the underlying numbers — hallucination is structurally blocked, not merely reduced.
Narrative inflates a contained loss into a system-wide cascade not supported by impact-graph propagation.
Narrative names a single-point-of-failure that the structural-risk surface does not identify.
Narrative invents a regime shift (rate cycle, vol regime, correlation regime) absent from the regime-detection output.
Narrative escalates severity beyond the calibrated score band — tail tone without tail evidence.
Narrative softens a CRITICAL-tier verdict into reassurance — the inverse failure mode of R-04, equally blocked.
Narrative direction contradicts the underlying signed metric (e.g., describes deterioration as improvement, or vice versa).
Narrative chains causality across a zero-impact node the graph does not validate as a transmitter.
S9 · Unified integration · convergence point · six sister modules · cross-domain synthesis
Module 01 is where the other six converge.
ARIN is the convergence point for the entire Sovereign Risk Infrastructure. The six sister production modules export analytical data to ARIN through private integration contracts, enabling cross-domain synthesis. ARIN is the central decision engine (Module 1 of 7); when sub-second risk math is required for a verdict, ARIN delegates to the ARIN22 deterministic kernel — ~99 µs per kernel call (CPU, D=4 to D=200), 1,008-job validated.
Inbound Data Sources
- Risk Analyzer — VaR / CVaR, stress-test results, portfolio metrics, factor decomposition
- Investment Analytics — institutional reports, Report Agent outputs, sector scans, watchlist signals
- News Analytics — sentiment scores, narrative events, RiskMirror alerts, impact-graph clusters
- Digital Assets Analytics — token ratings, DeFi protocol snapshots, on-chain anomalies
- Global Risk Intelligence — country / city / enterprise risk scores, multi-domain Unified Stress Report, digital-twin outputs
- External Feeds — authoritative public data sources (geological, hazard, health, macro, sanctions, vulnerability), with real-time ingestion
Output & Export
- Verdict Object — JSON payload with verdict, confidence, dissent map, per-agent assessments, entity-memory reference and audit hash
- Export Storage — durable persistence with entity binding (portfolio, symbol, ISIN, country code)
- Private Integration Contract — NDA-bound export and verdict retrieval surface for authorised modules
- WebSocket Stream — real-time verdict updates for portfolio-monitoring dashboards
- Audit Trail — immutable log of every agent assessment, Governor decision and data input for regulatory compliance
S10 · AI engine & infrastructure · 5-provider LLM sovereignty · H100 · NeMo Guardrails · GNN
5-provider LLM sovereignty. 8×H100. NeMo Guardrails. GNN over the Hydra Ontology.
LLM Sovereignty — 5-Provider Fallback
Zero single-vendor exposure. Hardware: 8× NVIDIA H100 80 GiB (DGX-class), 1.66 s mean inference latency, 1M-token KV cache for long-context retention. Multi-model routing per agent role (reasoning, general analysis, fast routing, structured extraction). Response caching, retry with exponential back-off, multi-provider load balancing. NVIDIA NeMo Guardrails govern every response for compliance, safety and factuality before it reaches the verdict pipeline. ARIN can run fully sovereign — no external API call required for a complete verdict cycle.
Graph & ML
Persistent graph store with multi-hop traversal and graph-neural-network layer for systemic-risk and contagion modelling. Classical ML stack for credit scoring and classification, plus deep-learning layer for non-linear factors. All compute GPU-optimised via NVIDIA CUDA. Specific libraries and engines are operational detail.
Backend Stack
Modern Python service layer with strict typed-schema validation and asynchronous workers. JWT authentication, OAuth-ready and OWASP-recommended password hashing. Polyglot persistence with relational primary store and in-memory hot store for Entity Memory. Specific framework choices are operational detail.
Frontend Stack
Modern React + TypeScript single-page application with type-safe state management and a Midnight Command design system. Multi-library charting and graph-visualisation surface for the Decision Flow, plus interactive 3D for entity exploration. Full responsive design.
S11 · Compliance & audit · 5 decision-integrity invariants · SR 26-2 / DORA / SEC 17a-4 / GDPR
5 decision-integrity invariants. Structural, not configurable.
Coverage: Basel III / IV · DORA · MiFID II · MiCA · SEC (17a-4) · FCA · GDPR / UK GDPR. Every verdict carries a per-decision provenance bundle: code_commit, calibration_run_id, model_versions, ethics_rules_version.
5 Decision-Integrity Invariants — structural properties of the system, not features:
Every analysis runs against a frozen input snapshot. The exact data at decision time is reproducible.
Every verdict is written to a SHA-256 hash chain. No edit, no delete — replay only.
Output never influences input. Contamination detection blocks closed-loop self-reference.
Every verdict passes through one policy gate (Meta-Decision Governor). No back-channel publication.
TTL checks on all input streams. Stale-input verdicts are blocked at ingest.
HITL Escalation Triggers — codified, not operator-defined: dissent ≥ 25%, SELL or AVOID combined with high confidence, any agent flagging CRITICAL-tier risk.
- Authentication — registration, login, JWT tokens, OAuth-ready; role-based access control (RBAC) with Admin, Analyst and Viewer roles managed in the relational primary store.
- GDPR Compliance — data access, export (JSON / CSV), deletion (right to erasure); audit log for all data-processing operations; configurable retention policies.
- Regulatory Audit Trail — immutable SHA-256 chain of every agent assessment, consensus computation, Governor decision and data input. 7-year retention per DORA Article 17 and SEC Rule 17a-4. Per-verdict provenance bundle attached to each entry.
- NeMo Guardrails + 7-Rule Audit Engine — LLM responses pass through NVIDIA NeMo Guardrails for compliance filtering, then through the math-to-narrative audit engine that blocks any narrative inconsistent with the deterministic state.
- Backup & Recovery — automated database backups, hot-store persistence, disaster-recovery procedures; 99.8% uptime SLO target.