Enterprise Architecture Overview

The Virtual Data Assistant (VDA) is designed from the ground up for mission-critical enterprise environments. It bridges traditional enterprise data stores, modern cloud warehouses, and autonomous AI agents through a secure, air-gapped, zero-trust control plane.

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1. Architectural Design Principles

The VDA platform adheres to seven foundational architecture principles:

flowchart TD
    P1["1. Zero-Trust Security<br/>(Every request authenticated & PBAC verified)"]
    P2["2. Air-Gapped Autonomy<br/>(Full functionality with 0 external network dependencies)"]
    P3["3. Data Sovereignty<br/>(No customer row data leaves on-premise perimeter)"]
    P4["4. Defense-in-Depth<br/>(TLS 1.3, SQL Security Firewalls, Column Masking)"]
    P5["5. Semantic Interoperability<br/>(Standard OpenLineage, OpenMetadata, MCP protocols)"]
    P6["6. Asynchronous Resilience<br/>(Durable, distributed workflow execution)"]
    P7["7. Multi-Model Persistence<br/>(Graph Lineage, Vector Search, Relational State)"]

    P1 --- P2 --- P3 --- P4 --- P5 --- P6 --- P7

1. Zero-Trust Security

No user, agent, or service is inherently trusted. Every API invocation, GraphQL query, and MCP tool call must present a cryptographically verified JWT or API token, which is evaluated against dynamic, role-bound Policy-Based Access Control (PBAC) rules.

2. Air-Gapped Autonomy

The platform operates with 100% feature completeness in disconnected (air-gapped) environments. All machine learning models (multilingual embeddings, PII classification, local LLMs) and runtime dependencies are pre-packaged into immutable container images operating with zero external network dependencies.

3. Absolute Data Sovereignty

VDA strictly separates metadata operations from data plane execution. Analytical prompts are converted to SQL using pruned metadata schemas. At no point is customer row data transmitted to external third-party AI services.

4. Defense-in-Depth

Protection is enforced across multiple tiers: transport encryption (TLS 1.3), network boundary isolation, enterprise identity federation, SQL query validation, column-level redaction, and immutable audit trails.

5. Open Standards & Interoperability

Built upon open enterprise standards: OpenLineage for operational metadata events, Model Context Protocol (MCP) for AI agent tooling, and Great Expectations for declarative data quality testing.

6. Asynchronous Resilience

Long-running operations — such as metadata harvesting, deep data profiling, embedding recalculation, and quality suite execution — are executed as fault-tolerant, durable distributed workflows with automated checkpointing and retries.

7. Multi-Model Persistence Architecture

Purpose-built storage engines optimized for specific enterprise workloads: - Operational Graph Engine: Stores operational pipeline lineage, asset dependencies, and entity relationships for sub-second traversal. - Vector Search Engine: High-speed vector index providing dense semantic representations for multilingual search and catalog discovery. - Relational Control Plane: ACID relational store for user accounts, role definitions, PBAC policies, SSM workbooks, and system configurations.

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2. Architecture Section Guide

Chapter	Focus
System Topology & Network Flows	Component tiers, internal network boundaries, service ports, and synchronous vs. asynchronous data paths.
Zero-Trust Security & PBAC Engine	The formal 4-tuple authorization model, AST-level SQL security firewall, and column-level masking mechanics.
AI Engine & Data Services	Dual-tier LLM task routing, vector embedding architecture, GLiNER NER model, and prompt token budgeting.
Air-Gapped Operation & Compliance	Security hardening, offline verification, banking regulation compliance (BCBS 239, GDPR), and zero-egress guarantees.
High Availability & Disaster Recovery	Clustering, failover, worker queue scaling, backup strategies, and recovery point objectives (RPO/RTO).