Compliant LLM

Compliant LLM is a Model Context Protocol (MCP) server designed to give developers, security teams, and compliance officers a single, unified platform for rigorously testing the safety and regulatory adherence of generative AI systems. By acting as a bridge between any LLM provider (via LiteLLM) and the AI assistant, it removes the need for custom tooling and lets teams focus on policy rather than plumbing.

The core problem this server solves is the fragmentation of security and compliance checks in modern AI workflows. When building or deploying an LLM‑powered agent, developers must simultaneously guard against prompt injection, jailbreak attempts, and data leakage while also proving that the system meets frameworks such as NIST, ISO, GDPR, or HIPAA. Traditional approaches require separate scripts, third‑party services, and manual reporting—each adding complexity and risk. Compliant LLM consolidates these concerns into a single MCP endpoint, exposing ready‑made attack vectors, compliance checklists, and detailed audit reports that can be consumed directly by an AI assistant or CI/CD pipeline.

Key capabilities are expressed in plain language:

• Security Testing – The server ships with more than eight attack strategies, including prompt injection, jailbreaking, and context manipulation. Developers can trigger these tests on demand or schedule them as part of a nightly build.
• Compliance Analysis – Built‑in checks map to major frameworks (NIST, ISO, OWASP, GDPR, HIPAA). The server evaluates prompt content, data handling, and response behavior against policy rules, producing compliance verdicts.
• Multi‑Provider Support – Whether you’re using OpenAI, Anthropic, Gemini, Mistral, or any of the other supported providers, the server normalizes interactions so that tests and reports are consistent across models.
• Visual Dashboard & Reporting – An interactive UI lets teams drill down into test results, view trends over time, and export comprehensive reports with actionable insights. These artifacts can be fed back into the MCP for automated policy enforcement.
• End‑to‑End Integration – By exposing a standard MCP interface, the server can be dropped into any AI assistant workflow. Agents can call the server to validate a prompt before sending it, or to verify that a response satisfies regulatory constraints.

Real‑world use cases include:

• Regulated Industries – Healthcare, finance, and government teams can run HIPAA or GDPR checks on their internal chatbots before deployment.
• Enterprise AI Ops – DevOps pipelines automatically run compliance and security tests on every new model version, ensuring no drift from policy.
• Open‑Source AI Projects – Community developers can share a single MCP instance that guarantees safe prompt usage across forks and integrations.
• MLOps Automation – CI/CD systems can call the server’s endpoints to gate merges, enforce policy compliance, and generate audit logs.

The unique advantage of Compliant LLM lies in its single‑stop, MCP‑native design. Unlike external services that require separate APIs or manual orchestration, this server plugs directly into the assistant’s context loop. It guarantees that every prompt and response is vetted against both security hardening and regulatory frameworks, all while supporting the full spectrum of popular LLM providers. This integration reduces friction for developers and provides a clear, auditable trail—essential for both internal governance and external certification.