Fusion MCP - Security & Governance Focus: Why build IT yourself?
"Most enterprise AI projects fail because LLMs cannot understand fragmented, legacy database schemas without costly manual mapping and massive security risks."
AIMLUX.ai - Equitus Fusion MCP delivers an automated, semantically aware context plane via reusable interconnection templates (KGNN). MCP converts complex legacy data into structured RDF triples, Fusion MCP slashes cloud token costs, enforces strict ICAM identity governance, and turns months of manual integration into a turn-key Service-as-Software deployment.
FUSION Model Context Protocol (MCP) Service as Software - Produces a Standardized Bridge, MC allows LLMs and AI agents to query operational systems, run tools, and fetch dynamic business context without writing fragile point-to-point ETL connectors.
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Section I: Develop, Deploy and Maintain your IBM DB2 SQL Migration, Integration and Cyber/Physical Security systems with AIMLUX.ai
1. Fusion MCP - Builds an Enterprise-Grade, Zero-Trust MCP layer using Arcxa and KGNN through four core capabilities:
Schema Discovery & Auto-Generated Tool Manifests (Arcxa):
: Building an MCP server manually requires defining JSON-RPC schemas and tool payloads for thousands of database tables (SAP HANA, Oracle PL/SQL, Db2).
Equitus Solution: Arcxa ingests and analyzes legacy relational schemas, stored procedures, and metadata. It automatically converts raw tables into standardized MCP Tool & Resource Specs (published standard JSON endpoints for LLMs).
2. Semantic Context & Knowledge Graph Control Plane (KGNN):
Challenge: Basic MCP servers simply pass raw SQL query results back to the LLM, causing hallucination when database column names are ambiguous (e.g.,
CUST_TYP_3).Equitus Solution: KGNN maps source records into a Triple-Store RDF network (Subject-Predicate-Object). When an MCP Host queries data through the Equitus MCP layer, KGNN attaches full semantic metadata to the payload, ensuring the LLM understands business definitions, context, and entities natively.
3. Multi-Source Hybrid Query Federation (Fusion MCP):
Challenge: Real-world enterprise requests span multiple isolated databases.
Equitus Solution: The Fusion MCP Layer exposes a single standardized connection interface. When an LLM executes an MCP tool call, Fusion orchestrates simultaneous read operations across legacy systems (SAP, Oracle) and target analytics clouds (Databricks, Snowflake) behind a single context window.
4. Zero-Trust Governance & Entitlements (ICAM + EVS):
Challenge: LLMs cannot inherit unrestricted access to underlying enterprise data sources.
Equitus Solution: ICAM enforces fine-grained Identity and Access Management directly inside the MCP payload header, while EVS monitors visual and cyber telemetry in real-time to intercept unauthorized data extraction attempts before they reach the model.
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SECTION II - Deployment Engineering Checklist: Key Questions Asked by Equitus Engineers
Begins Teaming by starting a migration readiness assessment - with an Equitus Automation Engineer (EAE) initiates a teaming connection for enterprise deployment alongside client IT staff, they ask structured diagnostic questions across five core categories:
1. Discovery & Legacy Source Systems
System Footprint: Which legacy database versions (Oracle PL/SQL, SAP HANA, IBM Db2) are currently live, and what are their existing transactional loads/IOPS constraints?
Procedural Logic: How much business logic is locked inside stored procedures, triggers, and package bodies versus external application layers?
Network & Connectors: What network security boundaries (VPCs, firewalls, site-to-site VPNs) exist between legacy on-prem host servers and cloud target environments?
2. Fusion MCP Layer & LLM Architecture
Host Application Ecosystem: Which AI Hosts/Agents will consume the MCP Layer (e.g., custom internal LLM, Claude Desktop/Enterprise, Cursor, OpenAI/Azure assistants)?
Transport & Security Protocol: Are MCP endpoints required to run via HTTP/SSE for remote services or local stdio connections inside isolated containers?
Rate & Token Limits: What are the throughput requirements for concurrent tool calls and payload sizes returning from large query joins?
3. Semantic & Mapping Layer (Arcxa + KGNN)
Ontology Standards: Does the client already maintain an enterprise data model/glossary, or should KGNN auto-extract relationships into RDF triples?
Mapping Validation Rules: What confidence threshold is required for Arcxa’s automated pre-mapping before a human engineer is required to validate schema conversions?
State Management: Should transformation states and lineage tracking be maintained in-memory during streaming ingestion or persisted in a dedicated RDF store?
4. Target Environment & Cloud Sync
Target Platforms: Which destination targets are in scope (Databricks Unity Catalog, Snowflake, AWS S3/Redshift), and what are their specific staging ingestion patterns?
Sync Strategy: Is this a one-time migration cutover or a continuous delta-synchronization process running during parallel ops?
5. Identity, Access, & Governance (ICAM & EVS)
RBAC Ingestion: How will client Active Directory, LDAP, or SAML/Okta identity groups map into ICAM roles within the MCP layer?
Audit & Lineage: What data residency, compliance frameworks (HIPAA, SOX, FedRAMP), and EVS audit telemetry recording levels are mandated for this deployment?
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