Connect LLMs to core systems with clear architecture layers, integration patterns, and security controls.
How do you connect LLMs to production systems without creating a security or audit gap?
Enterprise AI integration is the work of connecting language models, embedding services, and orchestration layers to existing applications, data stores, and identity systems so AI features run inside production workflows, not as standalone chat windows. Integration answers how requests flow from a CRM, portal, or internal tool through authentication, retrieval, model inference, and back to the system of record with auditable results.
This is distinct from selecting GenAI use cases or estimating business ROI. Integration focuses on architecture: which components exist, how they communicate, and what security boundaries apply at each hop.
Architecture Layers
A typical enterprise AI stack has four layers, each with clear interfaces:
Experience layer. Web apps, mobile clients, Slack bots, or embedded widgets where users trigger AI features. This layer handles session management and passes user context (role, tenant, locale) downstream.
Orchestration layer. API gateway, workflow engine, or agent framework that routes requests, enforces rate limits, selects models, and composes RAG retrieval with tool calls. Keeps business logic out of prompt strings where possible.
Model and retrieval layer. LLM endpoints (cloud API or self-hosted), vector databases, hybrid search, and optional fine-tuned adapters. Caches embeddings and manages index refresh schedules.
Systems of record. ERP, CRM, ticketing, document management, and data warehouses accessed through scoped APIs or read replicas. Writes should go through existing validation rules, not direct model-initiated mutations without guardrails.
Document these layers in ADRs so teams know where to add logging, encryption, and failover.
Integration Patterns
Synchronous API augmentation. User action triggers a single request-response cycle: summarize this case, classify this email. Low latency requirements favor smaller models or cached retrieval. Timeout and fallback behavior must be defined when the model or vector index is unavailable.
Event-driven processing. Messages on a queue trigger batch summarization, enrichment, or routing. Suits high volume and tolerates seconds to minutes of delay. Idempotency keys prevent duplicate side effects on retry.
RAG over internal corpora. Ingestion jobs sync documents into a search index with metadata for access control. Queries filter by user permissions before chunks reach the model. Refresh pipelines must track source deletions and permission changes.
Agentic tool use. Models call registered functions (create ticket, lookup account) with OAuth or service accounts limited to required scopes. Tool definitions should be minimal and validated; never expose raw SQL or shell to the model.
Choose patterns based on latency, audit requirements, and whether the workflow is read-only or mutating.
Security and Compliance Controls
Identity and authorization. Propagate corporate SSO claims through the orchestration layer. Enforce row-level and document-level access in retrieval, not only in the UI. Service accounts for batch jobs need rotation and least privilege.
Data residency and egress. Classify prompts, retrieved context, and logs by sensitivity. Block or redact PII before sending to external model APIs where policy requires. Private endpoints and VPC peering reduce exposure for regulated workloads.
Logging and audit. Store request IDs, model version, retrieved source IDs, and tool invocations. Retention must meet legal hold and privacy erasure requirements. Enable security teams to investigate prompt injection or data exfiltration attempts.
Supply chain. Pin model providers and dependency versions. Monitor for CVEs in inference servers and embedding libraries. Contract review for subprocessors and training data use clauses.
Threat model for LLM-specific risks. Prompt injection via user-controlled content, indirect injection via indexed documents, and excessive agency from over-permissioned tools are integration problems solved with input sanitization, retrieval filtering, output validation, and human approval gates on destructive actions.
Operational Concerns
Integration work continues after launch. Plan for model upgrades (regression tests), index rebuilds, cost caps per tenant, and circuit breakers when error rates spike. Runbooks should cover disabling AI features independently of core application uptime.
Related Reading
- How AI and GenAI Are Transforming Business Operations
- RAG and Agentic Workflows Explained
- MLOps Best Practices for the Enterprise
- AI and GenAI integration services
Contact Sea Wing AI to design an enterprise AI integration roadmap.