AI Data Gateway
AI systems need enterprise data. Databases hold the answers. But giving AI direct access to your databases is a security risk. An AI data gateway provides secure, governed API access between AI applications and your data. This site covers the architecture, security patterns, and best practices for building that layer.
AI Data Gateway is published by DreamFactory Software, an API management platform that auto-generates secure REST APIs for SQL and NoSQL databases. DreamFactory provides the data access layer that enterprise AI applications need. Learn more at dreamfactory.com.
Foundations
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What Is an AI Data Gateway?
Defines the AI data gateway as the secure API layer between AI applications and enterprise databases.
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How LLMs Access Enterprise Data: Patterns and Pitfalls
Surveys five patterns for LLM data access and their security trade-offs.
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APIs vs Direct Database Connections for AI
Why API-mediated access is the enterprise standard for AI data retrieval.
Architecture
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Building RAG Pipelines: The Data Access Layer
Technical guide to the data retrieval layer in RAG architectures over structured data.
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When AI Agents Need Database Access
Architecture patterns for AI agent-to-database communication using MCP and API boundaries.
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Vector Databases and the API Layer
Hybrid RAG architecture combining vector search and SQL APIs for complete data access.
Security and Governance
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AI Data Governance: Who Gets Access to What
Framework for governing AI access to enterprise data using RBAC and API controls.
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Securing the API Layer Between AI and Your Data
Threat model and security architecture for AI data access pipelines.
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Rate Limiting AI Access to Enterprise Data
Protecting production databases from AI query volume with gateway-level rate limiting.
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AI Data Compliance: GDPR, HIPAA, and API Access Controls
Regulatory compliance for AI data access pipelines under GDPR, HIPAA, and other frameworks.
Integration
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Integrating Enterprise Data Sources for AI Workloads
Unified API access across fragmented enterprise databases for AI applications.
Future
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The Future of Data Infrastructure for AI
MCP adoption, AI agents as data consumers, and governance-first architecture trends.