AI & Data
LLMs with Structured Data: Practical Enterprise Patterns
How to connect language models to enterprise data safely: retrieval, controlled SQL access, tool calling, authorization, validation and observability, and why most of the safety lives outside the model.
- Status
- Proposed
- Type
- Talk
- Formats
- Webinar · Panel · Internal session
- Level
- Intermediate
- Duration
- 45–60 minutes
This is a proposed session topic. No public event is currently scheduled.
Who it is for
- Engineers building assistants or AI features over company data
- Architects and security reviewers evaluating LLM applications
What attendees will learn
- The difference between retrieval, controlled SQL and tool calling, and when to use each
- Why the model should never hold a database connection
- How to enforce authorization and tenant isolation before data is read
- How to validate model output before it is shown or acted on
- What to log and observe, and what not to store
On this page
Session overview
Demos that connect a model to a database are easy. Systems that do it safely for many users and tenants are not. This session compares the main patterns for using LLMs with structured data. For each one it shows where the trust boundaries belong: identity before data access, bounded context for the model, and validation before anything reaches a user. The material builds on Using LLMs with Your Data: Practical Patterns.
User
- UI or API client
Application
- Orchestration
Auth
- Authentication
- Authorization
Tools
- Retrieval
- Controlled SQL
- Tool calls
Data
- Governed data platform
LLM
- Bounded context
Response
- Validated answer
Outline
- The important distinction: knowledge in documents vs facts in tables.
- Retrieval-augmented generation, and where it stops working.
- Controlled SQL and semantic layers: letting users ask questions without free-form SQL.
- Tool and function calling: typed tools, validation and least privilege.
- Security is an application responsibility: identity, tenants and prompt and data boundaries.
- Validation and observability: checking answers, tracing calls, controlling cost.
- When not to use an LLM at all.
Adapting the session
- Panel: works well as a discussion of security and governance trade-offs with practitioners from security or data governance.
Related articles
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Using LLMs with Your Data: Practical Patterns
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Planned articles on these topics
Related projects
AI Data Assistant
A reference application for safe LLM access to structured enterprise data: approved tools, authorization before data access, validated answers and full observability.
Modern Report Engine
A planned reference implementation for secure, tenant-aware analytical requests, cache decisions, Fabric queries, exports and operational monitoring.
SQL Performance Analyzer
A practical SQL Server and Python tool for investigating workload: active sessions, blocking, waits and expensive queries, grouped so the real problem stands out.