PolicyGuard

PolicyGuard

Provides policy-based access control, incident tracking, and compliance monitoring to govern AI agent behavior. It enables organizations to enforce security rules and maintain audit trails by validating agent actions against trust levels and pattern-based policies.

Category
Visit Server

README

PolicyGuard

Security & Governance MCP Server for AI Agents

Python 3.10+ MCP License: MIT

PolicyGuard is an MCP (Model Context Protocol) server that provides policy-based access control, incident tracking, and compliance monitoring for AI agents.

Built for MCP_HACK//26 hackathon - "MCP & AI Agents Starter Track" and "Secure & Govern MCP" categories.

Note: This project demonstrates working integration with kagent (AI agent platform) and can be extended with kgateway (API gateway) for additional network-level security.


Table of Contents


Overview

As AI agents become more autonomous, organizations need controls to govern their behavior. PolicyGuard is my first MCP server project - built to explore how security and governance can be implemented at the MCP layer.

The Problem

AI agents can call any tool they have access to. Without governance:

  • Agents might perform destructive operations
  • No audit trail of agent actions
  • No way to enforce security policies
  • No visibility into compliance

The Solution

PolicyGuard adds a security layer that agents call before taking action:

User Request → AI Agent → PolicyGuard (validate_action) → Allowed/Denied

Features

Feature Description
Policy Enforcement Validate actions against security rules
Trust Levels low, medium, high, admin hierarchy
Pattern Matching Wildcard patterns like delete_*, *_production
Auto-Registration Unknown agents get minimal trust
Incident Tracking Automatic violation logging
Audit Trail Complete action history
Compliance Dashboard Security metrics at a glance

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                         AI Agent / LLM                          │
│                                                                 │
│  "Before any action, call validate_action to check permission" │
└─────────────────────────────────────────────────────────────────┘
                              │
                              │ MCP Protocol
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                     PolicyGuard MCP Server                      │
│                                                                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │  validate   │  │   create    │  │   report    │             │
│  │   action    │  │   policy    │  │  incident   │             │
│  └─────────────┘  └─────────────┘  └─────────────┘             │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │  register   │  │  get_audit  │  │    get      │             │
│  │   agent     │  │    log      │  │ compliance  │             │
│  └─────────────┘  └─────────────┘  └─────────────┘             │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
                    ┌───────────────────┐
                    │   JSON Storage    │
                    │   (policies,      │
                    │    agents,        │
                    │    audit_log,     │
                    │    incidents)     │
                    └───────────────────┘

MCP Tools

PolicyGuard exposes 6 tools via MCP:

1. validate_action ⭐ Primary Tool

Check if an action is allowed before executing it.

{
  "action_type": "tool_call",
  "target": "delete_records",
  "agent_id": "my-agent"
}

Response:

{
  "action_id": "act_a1b2c3d4e5f6",
  "allowed": false,
  "reason": "Delete operations require admin trust level"
}

2. register_agent

Register an agent with a trust level.

{
  "agent_id": "data-processor",
  "name": "Data Agent",
  "trust_level": "medium"
}

3. create_policy

Create security rules.

{
  "policy_id": "block-deletes",
  "name": "Block Deletes",
  "rules": "[{\"condition\": {\"tool_pattern\": \"delete_*\"}, \"action\": \"deny\"}]"
}

4. get_audit_log

Query action history.

5. get_compliance_status

Get security dashboard metrics.

6. report_incident

Manually report security incidents.


Quick Start

Prerequisites

  • Python 3.10+
  • pip

Local Installation

# Clone
git clone https://github.com/PrateekKumar1709/policyguard.git
cd policyguard

# Setup
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

# Run
python src/main.py

Test the Tools

import json
from src.tools.validate_action import validate_action
from src.tools.register_agent import register_agent

# Register an agent
result = json.loads(register_agent.fn(
    agent_id="test-agent",
    name="Test Agent",
    trust_level="medium"
))
print(f"Registered: {result['agent_id']}")

# Validate an action
result = json.loads(validate_action.fn(
    action_type="tool_call",
    target="read_data",
    agent_id="test-agent"
))
print(f"Allowed: {result['allowed']}")

HTTP Mode

python src/main.py --transport http --port 8000

Kubernetes Deployment

PolicyGuard includes a Helm chart for Kubernetes deployment.

Using Kind

# Create cluster
kind create cluster --name policyguard

# Build and load image
docker build -t policyguard:latest .
kind load docker-image policyguard:latest --name policyguard

# Deploy
kubectl create namespace policyguard
helm install policyguard ./helm/policyguard -n policyguard

# Verify
kubectl get pods -n policyguard

Port Forward

kubectl port-forward -n policyguard svc/policyguard 8000:8000

Testing

Unit Tests

pytest tests/ -v

Test Results

tests/test_tools.py::TestValidateAction::test_allow_read_for_low_trust PASSED
tests/test_tools.py::TestValidateAction::test_deny_delete_for_low_trust PASSED
tests/test_tools.py::TestValidateAction::test_allow_delete_for_admin PASSED
tests/test_tools.py::TestValidateAction::test_deny_suspended_agent PASSED
tests/test_tools.py::TestRegisterAgent::test_register_new_agent PASSED
tests/test_tools.py::TestCreatePolicy::test_create_valid_policy PASSED
... (16 tests total)
============================== 16 passed ==============================

E2E Test Output

[1/6] register_agent      ✅ SUCCESS
[2/6] create_policy       ✅ SUCCESS  
[3/6] validate_action     ✅ ALLOWED (read_data)
[4/6] validate_action     ✅ DENIED (delete_records)
[5/6] report_incident     ✅ SUCCESS
[6/6] get_compliance_status ✅ SUCCESS

ALL 6 MCP TOOLS WORKING!

kagent Integration

PolicyGuard integrates with kagent for Kubernetes-native AI agent management.

Screenshots

Agent List - PolicyGuard agent registered and ready:

Agent List

Agent Tools - 6 PolicyGuard MCP tools available:

Agent Tools

Compliance Dashboard - Asking for compliance status:

Compliance Chat

Policy Validation - Action denied based on policy:

Validation Chat

Quick Setup

# 1. Install kagent CRDs
helm install kagent-crds ./kagent-reference/helm/kagent-crds -n kagent --create-namespace

# 2. Install kagent with Ollama (free local LLM)
helm upgrade --install kagent ./kagent-reference/helm/kagent -n kagent \
  --set providers.default=ollama \
  --set providers.ollama.model=qwen2.5:1.5b \
  --set tag=0.7.13

# 3. Deploy PolicyGuard
helm install policyguard ./helm/policyguard -n policyguard --create-namespace

# 4. Create RemoteMCPServer
kubectl apply -f - <<EOF
apiVersion: kagent.dev/v1alpha2
kind: RemoteMCPServer
metadata:
  name: policyguard
  namespace: kagent
spec:
  protocol: STREAMABLE_HTTP
  url: http://policyguard.policyguard:8000/mcp
  timeout: 30s
EOF

# 5. Create PolicyGuard Agent
kubectl apply -f - <<EOF
apiVersion: kagent.dev/v1alpha2
kind: Agent
metadata:
  name: policyguard-agent
  namespace: kagent
spec:
  description: "Security agent using PolicyGuard"
  type: Declarative
  declarative:
    modelConfig: "default-model-config"
    systemMessage: |
      You are a PolicyGuard Security Agent. Use the PolicyGuard tools to:
      - validate_action: Check if actions are allowed
      - register_agent: Register new agents
      - create_policy: Define security rules
      - get_compliance_status: View compliance dashboard
    tools:
    - type: McpServer
      mcpServer:
        name: policyguard
        kind: RemoteMCPServer
        apiGroup: kagent.dev
        toolNames:
        - validate_action
        - register_agent
        - create_policy
        - get_audit_log
        - get_compliance_status
        - report_incident
EOF

Verified Working

# Check status
$ kubectl get agents,remotemcpservers -n kagent
NAME                TYPE          READY   ACCEPTED
policyguard-agent   Declarative   True    True

NAME          PROTOCOL          URL                                       ACCEPTED
policyguard   STREAMABLE_HTTP   http://policyguard.policyguard:8000/mcp   True

# Invoke agent via CLI
$ kagent invoke -t "Get compliance status" --agent policyguard-agent

Response:
- Security Posture: Healthy
- Total Policies: 1, Enabled: 1  
- Total Incidents: 0
- Agents Registered: 0

# Test validation
$ kagent invoke -t "Can test-agent delete the database?" --agent policyguard-agent

Response:
The action delete_database was not allowed for test-agent.
Reason: Delete operations require admin trust level.

Access kagent UI

kubectl port-forward -n kagent svc/kagent-ui 8080:8080
# Open http://localhost:8080

Security Model

Trust Levels

Level Score Use Case
low 1 Unknown agents, read-only
medium 2 Verified agents
high 3 Trusted agents
admin 4 Full access

Policy Rules

{
  "condition": {
    "tool_pattern": "delete_*",
    "trust_level_below": "admin"
  },
  "action": "deny",
  "message": "Delete requires admin"
}

Evaluation Order

  1. Agent suspended? → DENY
  2. Tool in denied list? → DENY
  3. Tool not in allowed list? → DENY
  4. Policy match? → Apply rule
  5. Default → ALLOW

Project Structure

policyguard/
├── helm/
│   └── policyguard/          # Helm chart
│       ├── Chart.yaml
│       ├── values.yaml
│       └── templates/
├── src/
│   ├── main.py               # Entry point
│   ├── core/
│   │   ├── server.py         # MCP server
│   │   └── utils.py          # Utilities
│   └── tools/
│       ├── validate_action.py
│       ├── register_agent.py
│       ├── create_policy.py
│       ├── get_audit_log.py
│       ├── get_compliance_status.py
│       └── report_incident.py
├── tests/
│   └── test_tools.py         # 16 unit tests
├── Dockerfile
├── pyproject.toml
└── README.md

Technologies Used

  • FastMCP - Python MCP server SDK
  • Pydantic - Data validation
  • Helm - Kubernetes packaging
  • pytest - Testing

Future Improvements

  • [ ] Database backend (PostgreSQL)
  • [ ] Web dashboard UI
  • [ ] Prometheus metrics
  • [ ] RBAC integration
  • [ ] Policy versioning

License

MIT License


Hackathon

MCP_HACK//26 - "MCP & AI Agents Starter Track"

This is my first MCP server project! I built PolicyGuard to learn:

  • How MCP servers work
  • How to expose tools to AI agents
  • How to deploy MCP servers on Kubernetes
  • How security/governance can be implemented at the MCP layer

What I Built

  • ✅ 6 MCP tools for security governance
  • ✅ Policy engine with pattern matching
  • ✅ Trust level system
  • ✅ Audit logging and incident tracking
  • ✅ Helm chart for Kubernetes
  • ✅ 16 unit tests
  • ✅ kagent integration examples

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured