AI agents are increasingly being wired into production databases, but the current architecture is a security nightmare. Exposing raw connection strings to large language models creates a massive attack surface where a simple prompt injection could exfiltrate credentials. Enter db-mcp-gateway, a self-hosted, open-source solution that acts as a strict intermediary. It isolates credentials within the gateway process, ensuring that AI agents never see the database password or URL.

The Three-Pillar Security Model

The gateway’s architecture relies on credential isolation, identity-driven access control, and an immutable audit trail. In this flow, the AI agent communicates via the Model Context Protocol (MCP) to the gateway, which then talks to the database. Crucially, the gateway strips all credential information from logs and error messages. If an agent is compromised, it cannot leak connection strings because it never possessed them in the first place.

SSO and Config-as-Code Permissions

Authentication is handled through established SSO providers like Okta, Google Workspace, Entra, Authentik, or Keycloak. The login flow occurs in the browser, eliminating the need for embedded browsers within the agent itself. Permissions are defined in a YAML file stored in version control, aligning with GitOps practices. For example, a grant can restrict the 'backend-devs' group to read-only queries on specific schemas within 'production_postgres', with a hard row limit of 1000 and a mandatory reason field for each query.

Deployment and Audit Trail

Deploying the solution is as simple as pulling a single Docker container: docker pull ghcr.io/developerz-ai/db-mcp-gateway:1.1.1. The container runs its own PostgreSQL instance for state and audit logs, supporting both PostgreSQL and MongoDB backends. Every query is recorded with the SSO user, group, grant, and timestamp. These tamper-evident logs can be retrieved via the get_query_history endpoint, providing compliance-ready data without external certifications.

Key Takeaways

  • db-mcp-gateway isolates database credentials from AI agents, preventing prompt injection attacks from leaking connection strings.
  • Access control is managed via SSO integration and YAML-based config-as-code, allowing for GitOps-style peer review of permissions.
  • The system provides an immutable audit trail of all queries, including user, group, and timestamp, satisfying compliance needs without external certification.

The Bottom Line

If you are letting LLMs touch your production data without a gateway like this, you are playing with fire. This is the missing layer for secure AI workflows.