Good Morning Tron Community. As we wait for Ohio University Consulting Fellows to finish up market research on top of our REV1 qualitative research we wanted to start to address Tron DAO next feed back point. **
Enhance developer documentation and include relevant security or technical disclosures to build confidence.** @support.hackathon
We are pleased to announce **CORE!
CORA Oversight & Regulation Engine (CORE)**
CORE lets CORA bend without breaking compliance:
- Metaphorically flexible: domains can hot-swap prompts, models, or entire execution modes (local/remote/manifest) without code rewrites, yet every change is validated, approved, versioned, and auditable.
- Strict adherence: immutable logs, signed configs, human approvals, LangGraph traces, and Mermaid diagrams provide clear chain of custody for regulators and internal auditors.
- Expandable governance: if regulations evolve (e.g., new AI transparency mandates), we can add validation steps, approval rules, or additional logging fields without altering domain logic.
- Client policy adaptation: per-client overlays let us enforce existing external regulations (e.g., banned model providers, mandatory prompts) with zero impact on the client’s workflows—no retraining, no policy changes required. Domains gracefully degrade via configured fallbacks while monitoring accuracy gaps.
In short, CORE behaves like a flexible joint: compliance constraints form the hinge, and dynamic configuration provides controlled motion. We can pivot quickly when client or regulatory expectations shift, but every movement is measured, logged, and reviewable—meeting the high bar of the insurance and InsurTech ecosystem.
The following document describes CORE—the hybrid, security-tiered architecture that powers our LangGraph-based insurance and blockchain assistant. It captures all required components, control planes, observability hooks, and compliance guardrails. The design maximizes flexibility while preserving strict adherence to internal and external (client) regulations.**
**
SOC 2 Type II Readiness Checklist
- Executive sponsorship and governance charter covering CORE scope and responsibilities (CC1)
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Security policy framework approved, versioned, and reviewed at least annually
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Roles, responsibilities, and segregation of duties documented for engineering, compliance, and operations
- Formal risk assessment program capturing identification, analysis, mitigation plans, and follow-up tracking (CC3)
- Communication and training plan ensuring staff understand SOC 2 obligations, security policies, and reporting channels (CC2)
- Vendor and subservice provider management program with due diligence, contracts, SLAs, and monitoring (CC4)
- Logical access controls with documented provisioning/deprovisioning, MFA, RBAC reviews, and quarterly access recertifications (CC6)
- Change management lifecycle covering design reviews, approvals, testing, separation of duties, rollbacks, and evidence retention (CC8)
- System operations procedures for logging, monitoring, alerting, backup verification, and capacity/performance management (CC5)
- Incident response plan with detection, triage, communication, root-cause analysis, and lessons-learned tracking (CC7)
- Business continuity and disaster recovery strategy with RTO/RPO targets, tested playbooks, and documented results (A1/A2)
- Data governance and retention program addressing classification, encryption (in transit/at rest), and disposal (PI1/PII for Confidentiality & Privacy)
- Privacy program (if Trust Service Criteria include Privacy) covering consent, data subject rights, and cross-border transfer controls (P1–P6)
- Confidentiality controls for sensitive client information, including need-to-know enforcement and secure transmission/storage (C1–C2)
- Availability commitments met via redundancy, uptime monitoring, SLAs, and continuity plans (A1–A4)
- Evidence collection workflow ensuring artifacts are gathered continuously over the audit period with ownership and storage defined
- Independent auditor engagement, scope confirmation, timeline, and bridge letters for any carve-out subservice organizations
- Management assertion drafted, reviewed, and aligned with controls in scope for the Type II period
## TODO
- Map each CORE control to the specific SOC 2 criteria (CC1–CC9, A, PI, C, P) and document coverage status
- Build evidence catalog template (control description, owner, frequency, storage location) and populate for all checklist items
- Automate evidence capture where possible (config snapshots, log exports, approval records) and schedule manual collections
- Align CORE observability dashboards with SOC 2 reporting needs (uptime, security events, change records)
- Draft customer-facing summary of CORE SOC 2 commitments for onboarding and due diligence packages
# CORA Oversight & Regulation Engine (CORE)
## Hybrid Domain Hot-Swapping Architecture
This document describes CORE—the hybrid, security-tiered architecture that powers our LangGraph-based insurance assistant. It captures all required components, control planes, observability hooks, and compliance guardrails. The design maximizes flexibility while preserving strict adherence to internal and external (client) regulations.
-–
## 1. Architecture summary
1. **Config-driven governance layer** (PostgreSQL + signed artifacts)
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`domains` table stores `name`, `security_rating`, `graph_mode`, `status`, `fallback_domain`, `model_hierarchy`, `manifest_version`, `prompt_version`, `llm_parameters`, `change_ticket_id`, `last_reviewed_by`, and JSONB metadata.
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Version history tables persist immutable records of every config, manifest, and prompt change. Each entry includes hashes, automated validation results, and human approval signatures (two-person rule for `high`/`extreme`).
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Prompts and manifests live in object storage with HSM-backed signatures; the DB keeps metadata, state, and references.
2. **Runtime loader and registry**
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All domains inherit `ConfigDrivenDomain`, which enforces security tier behavior, model hierarchy selection, telemetry mix-ins, secrets injection, and hot-swapping.
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`DomainRegistry` holds active domain instances, resolves client-specific policies, and orchestrates fallback routing.
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Activation workflow: fetch config → verify signature → run smoke tests → log activation → swap orchestrator. Rollback to last-known-good on failure.
3. **Security tiers and hosting modes**
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`low`: in-process module (shared LangGraph, same service, built from local code).
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`medium`: separate graph per domain within the same service (isolation while remaining co-located).
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`high`: orchestrator invokes a remote service via authenticated RPC/REST (domain logic runs in dedicated infrastructure).
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`extreme` (and `test`/`dev`): dynamic graph composition at runtime from signed manifest + cloud storage assets (highest isolation & observability); debug logging enabled in `test`/`dev`.
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`disabled`: domain blocked; traffic routed to configured fallback chain (default `research` if unspecified).
4. **Model hierarchy & prompt management**
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Each domain/agent defines an ordered list of allowed LLM providers/models with optional prompt overrides and settings (temperature, max tokens, etc.).
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Client policy overlays remove disallowed models/providers while preserving fallback behavior.
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Prompt registry tracks versions and compatibility metadata; updates follow the same approval pipeline.
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## 2. Simplified graph layout by security tier
```mermaid
flowchart LR
subgraph CoreLangGraph\[LangGraph Runtime\]
direction TB
memory\[(Memory Subgraph)\] --> triage{Triage Node}
triage --> domainLow\["Low Tier Domain\\n(In-process code)"\]
triage --> domainMed\["Medium Tier Domain\\n(Local subgraph module)"\]
triage --> domainHigh\["High Tier Domain\\nRemote service call"\]
triage --> domainExt\["Extreme Tier Domain\\nDynamic manifest from cloud"\]
domainLow --> finalize((Finalize))
domainMed --> finalize
domainHigh --> finalize
domainExt --> finalize
end
classDef low fill:#d1f0ff,stroke:#0077b3,color:#000
classDef med fill:#e6ffd1,stroke:#4f8a10,color:#000
classDef high fill:#ffe6cc,stroke:#cc7a00,color:#000
classDef extreme fill:#ffd1dc,stroke:#b30047,color:#000
class domainLow low
class domainMed med
class domainHigh high
class domainExt extreme
%% Storage annotations
domainLow:::low -.->|Code| repo\[(Source repo)\]
domainMed:::med -.->|Config + Code| pg\[(PostgreSQL + local modules)\]
domainHigh:::high -.->|Signed manifest + API contract| svc\[(Remote domain service)\]
domainExt:::extreme -.->|Signed manifest + prompts\\n in cloud storage| bucket\[(Cloud storage)\]
```
- **Low tier** domain logic lives inside the main service’s source tree.
- **Medium tier** keeps code locally but uses per-domain graph builds driven by config.
- **High tier** executes via remote service endpoints (signed, audited contracts).
- **Extreme tier** materializes graphs dynamically from manifests and assets stored in signed cloud buckets.
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## 3. Operational controls & required parts
### 3.1 Configuration & workflow
- Draft config/manifest/prompt → automated validation (schema lint, unit/integration tests, security scans) → human approval → activation.
- Approvals link to ticket IDs and record multi-factor approver identity.
- Observability agents submit suggested changes through the same workflow (human-in-loop maintained).
### 3.2 Runtime enforcement
- Base class injects telemetry mix-ins, secret fetchers (Google Cloud Secret Manager), and security checks before executing domain logic.
- Domain activation events, fallback decisions, and model selections are logged and metered.
- Fallback matrix prevents cycles and ensures graceful degradation (e.g., insurance → legal → research).
### 3.3 Secrets & client policy overlays
- Secrets (LLM keys, API tokens, DB credentials) stored in GCP Secret Manager; domain runtime fetches on demand with principle-of-least-privilege IAM.
- Client policies overlay domain/model restrictions without code changes; same workflow updates ensure auditability.
### 3.4 Model hierarchy & monitoring
- Telemetry tags capture `model_name`, `prompt_version`, `fallback_used`, enabling performance comparisons against baselines.
- Shadow evaluations / A/B testing possible: run fallback model in parallel (outside user flow) to gauge quality before promotion.
- Underwriters can flag low-quality responses, feeding back into prompt tuning pipeline.
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## 4. Logging, observability, and oversight
### 4.1 LangGraph tracing
- LangGraph runtime emits structured events for every node, tool, and LLM call (with `domain`, `node`, `model`, `prompt_version`, `config_version`, `client_id`, `security_rating`, `run_id`).
- Events stream to LangGraph Studio (or OSS UI) for run replay and step-by-step inspection.
- Custom callbacks mirror events to telemetry bus for long-term analysis.
### 4.2 Logfire structured logging
- Unified JSON schema for access logs, security logs, config changes, and LLM activity.
- Append-only storage with hashing and retention policies meeting insurance regulations (7–10+ years).
- Key LangGraph trace events mirrored into Logfire so replay evidence exists even outside the tracing UI.
### 4.3 Per-run Mermaid graph snapshots
- Graph builder exports node/edge data; utility renders Mermaid diagram (as shown above) with metadata (ratings, fallbacks).
- Diagram and rendered SVG stored per run (`graph_version`, `run_id`) in audit storage.
- Run metadata includes a pointer to the Mermaid artifact so operators can switch between flow view and trace effortlessly.
### 4.4 Oversight and A/B testing
- Metrics pipeline (Prometheus/OpenTelemetry) tracks LLM usage, per-domain throughput, fallback rates, accuracy/latency deltas across models, security tool usage, and memory QA stats.
- Change-control UI allows safe experimentation through staged rollouts, shadow evaluation, and comparison dashboards.
- Evidence Locker viewer provides compliance teams with immutable logs, trace histories, Mermaid diagrams, and approval records for each change.
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## 5. Internal tooling
| Tool | Purpose | Primary Users |
|------|---------|----------------|
| **Config & Governance Console** | Edit configs/manifests/prompts, view validation results, capture approvals, trigger rollbacks/disable domains. | Platform engineers, compliance, domain owners |
| **Observability & Ops Portal** | Monitor real-time metrics, alerts, and run traces; link to LangGraph replay and Mermaid diagram. | SREs, ML operations, on-call staff |
| **Prompt & Model Tuning Lab** | Manage prompt hierarchy, run A/B or shadow tests, gather feedback, and submit change requests. | Prompt engineers, ML researchers |
| **Client Policy Dashboard** | View/manage per-client domain/model restrictions and fallback mapping. | Account managers, compliance |
| **Evidence Locker Viewer** | Search immutable logs, approvals, LangGraph traces, Mermaid snapshots; export for auditors/regulators. | Compliance, internal audit |
-–
## 6. Security stance and mitigations
| Risk / Requirement | Mitigation |
|--------------------|------------|
| Unauthorized config changes | Signed artifacts, workflow enforcement with multi-factor approvals, immutable version history |
| Secret leakage | Google Cloud Secret Manager, least-privilege IAM, no secrets in repo or logs |
| Domain compromise | Security ratings enforce hosting isolation; remote services use mTLS + signed contracts; fallback routes isolate issues |
| Prompt/model drift | Hierarchy monitoring, fallback performance metrics, human-in-loop prompt updates, shadow evaluations |
| Observability tampering | Dual logging (LangGraph + Logfire) with append-only storage, hashed logs, long-term retention |
| Regulatory audits | Evidence Locker, Mermaid snapshots per run, documented approvals, chain-of-custody metadata |
-–
## 7. Flexibility with compliance
CORE bends without breaking compliance:
- **Metaphorically flexible**: domains can hot-swap prompts, models, or entire execution modes (local/remote/manifest) without code rewrites, yet every change is validated, approved, versioned, and auditable.
- **Strict adherence**: immutable logs, signed configs, human approvals, LangGraph traces, and Mermaid diagrams provide clear chain of custody for regulators and internal auditors.
- **Expandable governance**: if regulations evolve (e.g., new AI transparency mandates), we can add validation steps, approval rules, or additional logging fields without altering domain logic.
- **Client policy adaptation**: per-client overlays let us enforce existing external regulations (e.g., banned model providers, mandatory prompts) with zero impact on the client’s workflows—no retraining, no policy changes required. Domains gracefully degrade via configured fallbacks while monitoring accuracy gaps.
In short, CORE behaves like a flexible joint: compliance constraints form the hinge, and dynamic configuration provides controlled motion. We can pivot quickly when client or regulatory expectations shift, but every movement is measured, logged, and reviewable—meeting the high bar of the insurance and InsurTech ecosystem.
and here is what GPT found comparable to CORE… it got compared to Microsoft, Salesforce, and IBM straight out the gate:
Regulated FinServ reference implementations (public case studies)
Examples:
- Capital One’s “Responsible AI governance architecture” (AWS re:Invent 2023 talk).
- JP Morgan’s “Model Risk Management for Generative AI” blueprint (Risk.net 2024).
Parallels: Multi-tier isolation, strict change control with ticket linkage, fallback hierarchies across approved models, and immutable logging targeted at regulatory audits.
Differences: Typically built on internal orchestration frameworks or AWS Bedrock guardrails; graph-based routing like CORE is less emphasized.
Takeaways for CORE
There’s a growing pattern of policy-driven orchestrators with artifact signing, dual observability, and human approvals—the same pillars CORE prioritizes.
Where others lean on specific cloud ecosystems, CORE’s LangGraph-centric approach gives you more runtime flexibility (dynamic manifests, per-domain hot-swaps) but still aligns with the same governance principles.
