Secure AI-Driven Database Access with db-mcp-gateway
Secure AI-Driven Database Access with db-mcp-gateway Introduction AI agents often need to query production databases, but exposing connection strings creates a serious security risk. db-mcp-gateway solves th
Secure AI-Driven Database Access with db-mcp-gateway
Introduction
AI agents often need to query production databases, but exposing connection strings creates a serious security risk. db-mcp-gateway solves this problem by acting as a trusted intermediary that never reveals credentials to the agent. The gateway is self-hosted, runs in a single Docker container, and supports PostgreSQL and MongoDB back-ends.
Credential Isolation
All database passwords are stored only inside the gateway. An AI agent sends a request using the MCP protocol, the gateway authenticates the request, executes the query, and returns only the result set. No log line or error message ever contains a connection string. This design eliminates credential leakage from laptops, CI pipelines, or the agents themselves.
SSO-Driven Authentication
The gateway integrates with popular SSO providers such as Okta, Google Workspace, Entra, Authentik and Keycloak. Authentication happens via a browser-based flow; no embedded browsers are required. Permissions are granted per group, and the gateway validates the user in real time for each request.
grants:
- group: backend-devs
databases: [production_postgres]
actions: [query_read]
constraints:
schemas: [public, analytics]
row_limit: 1000
require_reason: true
The YAML file lives in version control, allowing audit-ready reviews and GitOps workflows.
Audit Trail
Every query is recorded with the SSO user, group, grant, timestamp and the executed SQL. The audit logs are stored in a PostgreSQL table inside the gateway, providing a tamper-proof history that can be exported for compliance reporting. Although the gateway is not itself certified, the detailed logs support SOCโฏ2, HIPAA, ISOโฏ27001 and other frameworks.
Config-as-Code Permissions
Permissions are defined as code, reviewed through pull requests, and applied without a UI. This approach reduces human error and ensures that changes are tracked in the same repository as the gateway configuration.
Deployment
Deploying the gateway is straightforward:
# Pull the latest image
Docker pull ghcr.io/developerz-ai/db-mcp-gateway:1.1.1
# Run with your config
Docker run -p 8080:8080 -v $(pwd)/config.yaml:/app/config.yaml ghcr.io/developerz-ai/db-mcp-gateway:1.1.1
The container starts a single HTTP endpoint that implements the MCP protocol. The gateway state and audit logs are persisted in a PostgreSQL database.
Conclusion
db-mcp-gateway provides a security-first bridge between AI agents and production databases. By keeping credentials inside the gateway, enforcing SSO authentication, and logging every query, it meets the needs of Platform/SRE teams, backend developers and security officers. Try it today and protect your data without sacrificing AI productivity.
Originally published by Dev.to Security. Aggregated on AIWithGhost for educational purposes โ full credit and traffic to the original publisher.