Enterprise data architecture
AI Data Gateway: Governed Enterprise Data Access for AI
A practical architecture for letting AI agents, analytics, dashboards, and workflows consume stable business capabilities without owning backend credentials, database schemas, or integration contracts.
Why the gateway exists
Agents should consume business capabilities, not private systems.
Enterprise data lives across databases, APIs, ERP and CRM platforms, files, and cloud services. Direct agent-to-system coupling duplicates credentials, fragments permission checks, repeats query logic, hides audit context, and makes write access too risky to explain.
The AI Data Gateway is the governed integration boundary. It turns user intent into approved business objects and capability calls, then applies policy, validation, lineage, and audit before any result reaches an AI application.
Diagram 1
The gateway is the controlled boundary between enterprise systems and AI applications.
Systems remain behind integration contracts. AI experiences call stable governed capabilities.
- PostgreSQL / MySQL
- SQL Server / Oracle
- REST / business APIs
- ERP / CRM
- Files / cloud sources
- AI agents
- Analytics
- Reports
- Workflows
- Dashboards
Diagram 2
Inside the gateway, natural language becomes a validated capability request.
This is not unrestricted text-to-SQL. The request moves through semantic mapping, policy, connector selection, validation, and evidence.
- User / agent intent
- Semantic intent
- Approved business objects / schema
- Authorization + policy
- Connector / capability selection
- Parameterized read or governed action
- Enterprise source
- Validated result
- Evidence / lineage / audit
- Agent response
Diagram 3
Concrete query walkthrough: first-quarter sales for top enterprise customers.
A governed enterprise query should show exactly how business intent becomes an authorized capability call, how the source response is constrained, and what evidence travels back with the answer.
- Intent recognitionSales / Customer / Revenue / Q1
- Permission checkUser can view approved sales summary fields
- Semantic mappingRevenue and customer tiers map to governed reporting objects
- Source resolutionGateway selects the approved sales/customer capability
- Bounded queryParameterized read with limits and expected shape
- Validated resultRows, totals, source metadata, and quality checks
- ResponseAnswer, chart, or report with provenance
1. Intent to contract call
gateway.resolve_intent(
text="Show me first-quarter sales for our top five enterprise customers",
user_id="u_1427",
tenant_id="acme"
)2. Capability policy YAML
capability: find_sales
mode: read
business_object: sales_summary
required_permission: sales.summary.read
filters:
quarter: Q1
customer_segment: enterprise
limit: 53. Approved object map
semantic_map:
sales: sales_summary
revenue: net_revenue
top_customers: customer_rank
first_quarter: fiscal_q1
blocked_fields:
- margin_private
- contract_terms4. Parameterized read
gateway.execute_read(
capability="find_sales",
params={"quarter": "Q1", "segment": "enterprise", "limit": 5},
include=["customer_name", "revenue", "source_system"]
)5. Example table return
| Customer | Q1 Revenue | Source | Lineage |
|---|---|---|---|
| Northwind Group | $4.8M | Sales API | trace_7f31 |
| Contoso Health | $4.1M | Warehouse | trace_7f31 |
| Fabrikam Energy | $3.7M | Sales API | trace_7f31 |
6. Evidence envelope
{
"answer_type": "sales_summary",
"rows_returned": 5,
"policy": "sales.summary.read",
"lineage": ["sales_api.orders", "warehouse.customer_rank"],
"trace_id": "trace_7f31"
}Diagram 4
Read paths and consequential action paths are governed differently.
The model can help reason, draft, or propose. Deterministic systems validate and execute approved business actions.
"Show me Q1 sales"
- Authorize
- Query
- Validate
- Respond with evidence
"Create a purchase order for supplier X"
- Resolve governed business capability
- Validate required fields
- Apply authorization and policy
- Request human approval when required
- Execute deterministically with idempotency
- Record audit and result
Capability model
Stable business capabilities replace backend coupling.
The capability contract is the interface AI applications use. Connectors own database dialects, API mechanics, source-specific permissions, error behavior, and operational details.
Capability
Find Order
Resolve a business request to an approved order object instead of exposing table structure.
Capability
Get Customer
Return customer fields allowed by user role, tenant, source policy, and requested purpose.
Capability
Find Sales
Map period, revenue, and customer language to governed reporting objects and bounded reads.
Capability
Check Inventory
Use a stable capability contract while connector logic stays behind the gateway boundary.
Capability
Create Purchase Order
Separate proposal, policy, approval, deterministic execution, and audit for consequential actions.
Example contracts
What the code boundary can look like.
These examples illustrate the interface shape. They are not production credentials, customer schemas, or a claim that the full gateway runtime ships in this WordPress page.
Business capability
{
"name": "find_sales",
"mode": "read",
"business_object": "sales_summary",
"allowed_fields": ["customer_name", "quarter", "revenue"],
"required_permissions": ["sales.summary.read"],
"max_rows": 5
}Policy-checked read
request = gateway.authorize(
user=user,
capability="find_sales",
purpose="quarterly_review"
)
result = gateway.execute_read(
request=request,
parameters={"quarter": "Q1", "segment": "enterprise"},
include_lineage=True
)Governed action
{
"capability": "create_purchase_order",
"mode": "action",
"idempotency_key": "po-2026-09-06-001",
"approval": "required",
"executor": "deterministic_service",
"audit": {"trace_id": "gw_7f31"}
}Semantic access layer
Natural language maps to approved business concepts, objects, and schema.
The semantic layer interprets terms like revenue, customer, supplier, inventory, quarter, region, and status against approved objects. It should reject or narrow ambiguous requests rather than handing arbitrary text to a database.
That mapping keeps backend implementation hidden while still giving agents enough structure to answer useful business questions.
- Approved objects before source access
- Field and row boundaries tied to identity
- Parameterized reads, not raw prompt-built SQL
- Source metadata attached to results
- Explicit failure messages for denied or ambiguous requests
- Human approval before consequential action
Security and governance
The LLM is never the security boundary.
Security belongs in authenticated services, policy checks, connector contracts, validation, approvals, lineage, and audit trails.
Authentication and authorization
Identity, RBAC, and policy are gateway concerns. The model does not decide who can see or change enterprise records.
Read and action boundaries
Read paths can be bounded and validated. Consequential action paths require stricter field validation, approval, idempotency, and audit.
Secrets outside prompts
Backend credentials, connector details, and privileged source contracts stay outside browsers, prompts, and WordPress presentation code.
Lineage and observability
Useful responses need source metadata, traceable transformations, known failure contracts, and reviewable audit evidence.
Reliability and platform engineering
Governed access also needs predictable operations.
Gateway capabilities should return known success and failure contracts, isolate dependency failures, apply bounded retries where safe, use idempotency for write operations, and expose monitoring signals tied to traceable request IDs.
What this enables
Use one governed data boundary across many AI experiences.
The same gateway pattern can support assistants, enterprise agents, finance and operations workflows, analytics, dashboards, reporting, and future MCP or tool interfaces.