aoc/AGENTS.md

Admin Operations Center (AOC)

Project Overview

AOC is a FastAPI microservice that ingests Microsoft Entra (Azure AD) audit logs, Intune audit logs, and Exchange/SharePoint/Teams admin audits (via the Office 365 Management Activity API) into MongoDB. It deduplicates events, enriches them with readable names from Microsoft Graph, and exposes a REST API plus a minimal web UI for searching, filtering, and reviewing events.

Technology Stack

• Runtime: Python 3.11 (3.14 for tests)
• Web Framework: FastAPI + Uvicorn (Gunicorn in production)
• Database: MongoDB (PyMongo)
• Frontend: Alpine.js + HTML/CSS (served as static files from backend/frontend/)
• Authentication: Optional OIDC Bearer token validation against Microsoft Entra (using python-jose and MSAL.js on the frontend)
• External APIs: Microsoft Graph API, Office 365 Management Activity API, Azure OpenAI / MS Foundry
• Deployment: Docker Compose (dev), Docker Compose + nginx (prod)
• CI/CD: Gitea Actions (lint + test + Docker build + release)

Project Structure

backend/
  main.py              # FastAPI app, router registration, background periodic fetch
  config.py            # Pydantic Settings configuration (loads .env)
  database.py          # MongoClient setup (db = micro_soc, collection = events)
  auth.py              # OIDC Bearer token validation, JWKS caching, role/group checks
  requirements.txt     # Python dependencies
  Dockerfile           # python:3.11-slim image, non-root user, version baked at build
  mcp_server.py        # Standalone MCP server for Claude Desktop / Cursor integration
  routes/
    fetch.py           # GET /api/fetch-audit-logs, run_fetch()
    events.py          # GET /api/events, GET /api/filter-options, PATCH tags, POST comments
    config.py          # GET /api/config/auth, GET /api/config/features
    ask.py             # POST /api/ask — natural language query with LLM
    health.py          # GET /health, GET /metrics
    rules.py           # Rule-based alerting endpoints
    webhooks.py        # Microsoft Graph change notification webhooks
  graph/
    auth.py            # Client credentials token acquisition for Graph
    audit_logs.py      # Fetch and enrich directory audit logs from Graph
    resolve.py         # Resolve directory object IDs to human-readable names
  sources/
    unified_audit.py   # Office 365 Management Activity API (Exchange/SharePoint/Teams)
    intune_audit.py    # Intune audit events from Graph
  models/
    event_model.py     # normalize_event() — transforms raw events to stored schema
  mapping_loader.py    # Loads mappings.yml (cached) with fallback defaults
  mappings.yml         # User-editable category labels and summary templates
  maintenance.py       # CLI for re-normalization and deduplication of stored events
  frontend/
    index.html         # Single-page UI with filters, pagination, ask panel, raw-event modal
    style.css          # Dark-themed stylesheet

Configuration

Copy .env.example to .env at the repo root and fill in values:

cp .env.example .env

Key variables:

• TENANT_ID, CLIENT_ID, CLIENT_SECRET — Microsoft app registration credentials (application permissions)
• AUTH_ENABLED — set true to protect API/UI with OIDC Bearer tokens
• AUTH_TENANT_ID, AUTH_CLIENT_ID — token validation audience/issuer
• AUTH_ALLOWED_ROLES, AUTH_ALLOWED_GROUPS — comma-separated access control lists
• ENABLE_PERIODIC_FETCH, FETCH_INTERVAL_MINUTES — background ingestion scheduler
• MONGO_ROOT_USERNAME, MONGO_ROOT_PASSWORD, MONGO_PORT — used by Docker Compose for MongoDB
• AI_FEATURES_ENABLED — set false to completely disable AI endpoints and UI (default true)
• LLM_API_KEY, LLM_BASE_URL, LLM_MODEL, LLM_MAX_EVENTS, LLM_TIMEOUT_SECONDS — LLM provider settings
• LLM_API_VERSION — required for Azure OpenAI / MS Foundry endpoints

Build and Run Commands

Docker Compose (recommended):

docker compose up --build

• API/UI: http://localhost:8000
• MongoDB: localhost:27017

Local development (without Docker):

# 1) Start MongoDB
docker run --rm -p 27017:27017 -e MONGO_INITDB_ROOT_USERNAME=root -e MONGO_INITDB_ROOT_PASSWORD=example mongo:7

# 2) Run backend
cd backend
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
export $(cat ../.env | xargs)
uvicorn main:app --reload --host 0.0.0.0 --port 8000

API Endpoints

• GET /api/fetch-audit-logs?hours=168 — pulls last N hours (capped at 720 / 30 days) from all sources, normalizes, dedupes, and upserts into MongoDB
• GET /api/events — list stored events with filters (service, actor, operation, result, start, end, search) and cursor-based pagination
• GET /api/filter-options — best-effort distinct values for UI dropdowns
• GET /api/config/auth — auth configuration exposed to the frontend
• GET /api/config/features — feature flags (ai_features_enabled)
• POST /api/ask — natural language query; returns LLM narrative + referenced events (only when AI_FEATURES_ENABLED=true)
• GET /health — liveness probe with DB connectivity
• GET /metrics — Prometheus metrics

MCP Server

A standalone MCP server (backend/mcp_server.py) exposes audit log tools for Claude Desktop, Cursor, and other MCP clients.

Available tools:

• search_events — Search by entity, service, operation, result, time range
• get_event — Retrieve a single event by ID (raw JSON)
• get_summary — Aggregated counts by service, operation, result, actor
• ask — Natural language question (returns recent events + guidance)

Claude Desktop config (~/.config/claude/claude_desktop_config.json):

{
  "mcpServers": {
    "aoc": {
      "command": "python",
      "args": ["/path/to/aoc/backend/mcp_server.py"],
      "env": {"MONGO_URI": "mongodb://root:example@localhost:27017/"}
    }
  }
}

The MCP server imports database.py directly and does not go through the FastAPI layer, so it shares the same MongoDB connection but bypasses auth.

AI Feature Flag

Set AI_FEATURES_ENABLED=false in .env to:

• Prevent the ask router from being registered in FastAPI
• Hide the "Ask a question" panel in the frontend
• Return ai_features_enabled: false from /api/config/features

This is intended for the open-core monetization split: core features (ingestion, filtering, search, export) are always available; premium AI features (NLQ, MCP) can be disabled.

Code Conventions

• Python modules use absolute imports within the backend/ package (e.g., from graph.auth import get_access_token). When running locally, ensure the working directory is backend/ so these resolve correctly.
• The project uses ruff for linting and formatting. Run ruff check . && ruff format . before committing.
• Keep changes consistent with the existing style: simple functions, explicit exception handling, and informative docstrings.
• The frontend is a single HTML file with inline JavaScript and Alpine.js.

Testing

Tests run with pytest and mongomock (no real MongoDB required):

cd backend
python -m venv .venv_test
source .venv_test/bin/activate
pip install -r requirements.txt
pytest tests/ -q

When adding new features or bug fixes, add or update tests in backend/tests/. The test suite covers:

• Event normalization and deduplication
• Auth middleware and token validation
• API endpoints (/api/events, /api/fetch-audit-logs, /api/ask)
• NLQ time range extraction, entity extraction, query building

Security Considerations

• Secrets: CLIENT_SECRET, LLM_API_KEY, and other credentials come from .env. Never commit .env.
• Auth validation: When AUTH_ENABLED=true, the backend fetches JWKS from https://login.microsoftonline.com/{AUTH_TENANT_ID}/v2.0/.well-known/openid-configuration, caches keys for 1 hour, and validates tenant/issuer claims. Tokens are decoded without strict signature verification (jwt.get_unverified_claims), so the tenant and issuer checks are the primary gate.
• Role/Group gating: Access is allowed if the token’s roles intersect AUTH_ALLOWED_ROLES or groups intersect AUTH_ALLOWED_GROUPS. If neither list is configured, all authenticated users are allowed.
• Pagination limits: page_size is clamped to a maximum of 500 to prevent large queries.
• Fetch window cap: hours is clamped to 720 (30 days) to avoid runaway API calls.
• MCP server: The MCP server bypasses auth entirely. Only run it in trusted environments or behind a VPN.

Maintenance and Operations

The backend/maintenance.py script provides two CLI commands useful for backfilling or correcting stored data:

# Re-run Graph enrichment + normalization on stored events
docker compose run --rm backend python maintenance.py renormalize --limit 500

# Remove duplicate events based on dedupe_key
docker compose run --rm backend python maintenance.py dedupe

Both commands operate directly against the MongoDB collection configured in config.py.