⚡ ARIA - Autonomous Risk Investigation Agent

AI-powered security threat investigation system that automatically detects anomalies, investigates them intelligently, and generates actionable incident reports in plain English.

Detects suspicious authentication behavior (impossible travel, credential stuffing, off-hours access) → Investigates using Claude LLM → Generates comprehensive incident reports with MITRE ATT&CK mapping → Real-time dashboard updates.

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Features

• 🤖 LLM-Powered Investigation - Uses Groq's Llama 3.3 to investigate anomalies and generate human-readable reports
• 🎯 Anomaly Detection - Isolation Forest ML detection for behavioral anomalies (geolocation, timing, failed attempts)
• ⚡ Real-Time Updates - WebSocket-powered dashboard with sub-100ms incident delivery
• 🛡️ Security Context - Maps detected threats to MITRE ATT&CK techniques for threat intelligence
• 📊 Live Dashboard - React UI showing incidents with severity levels, confidence scores, and detailed analysis
• 🐳 Production Ready - Docker Compose setup, deployed backend, PostgreSQL persistence
• 📈 Extensible - Pluggable detection layer, easy to add new anomaly types and investigation rules

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How It Works

Authentication Event
        ↓
   Detection Engine (Isolation Forest)
        ↓
  Anomaly Flagged? (Severity + Confidence)
        ↓
  ARIA Investigation Agent
    ├─ Step 1: Triage (classify severity)
    ├─ Step 2: Context Analysis (pull surrounding logs, user baseline)
    └─ Step 3: Report Generation (LLM investigation + MITRE mapping)
        ↓
  Incident Report (JSON + Plain English)
        ↓
  WebSocket Broadcast → Real-Time Dashboard
        ↓
  Security Team Takes Action

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Tech Stack

Backend

• Framework: FastAPI (Python async web framework)
• Database: PostgreSQL (Neon cloud)
• LLM: Groq API (Llama 3.3 model)
• ML: scikit-learn (Isolation Forest)
• Real-time: WebSockets
• Deployment: Docker, Render

Frontend

• UI Framework: React
• WebSocket Client: Native WebSocket API
• HTTP Client: Axios
• Styling: CSS-in-JS

Infrastructure

• Containerization: Docker & Docker Compose
• Version Control: Git/GitHub
• Cloud Database: Neon (PostgreSQL)
• Hosting: Render (backend)

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Quick Start

Prerequisites

• Python 3.11+
• Node.js 18+
• Docker & Docker Compose
• Groq API key (free tier: console.groq.com)
• Neon PostgreSQL connection string (free tier: neon.tech)

Local Development (Docker)

# Clone the repo
git clone https://github.com/Ayon99/ARIA
cd ARIA

# Set up environment variables
cp .env.example .env
# Edit .env with your Groq API key and Neon connection string

# Start all services with Docker Compose
docker-compose up

# Open dashboard
# Backend API: http://localhost:8000/docs
# Frontend: http://localhost:3000

Manual Setup (No Docker)

# Backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn main:app --reload

# Frontend (in separate terminal)
cd dashboard
npm install
npm start

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Project Structure

aria/
├── main.py                 # FastAPI app, endpoints, WebSocket handler
├── simulator/
│   └── log_generator.py    # Generate synthetic attack events for testing
├── agent/
│   └── investigator.py     # LLM investigation pipeline (triage → report)
├── database/
│   └── db.py              # PostgreSQL operations, schema management
├── detector/              # (Phase 2) Isolation Forest integration
│   ├── ml_model.py
│   ├── ml_features.py
│   └── detector.py
├── dashboard/             # React frontend
│   ├── src/
│   │   ├── App.js        # Main dashboard component
│   │   └── index.js
│   ├── package.json
│   └── Dockerfile
├── docker-compose.yml    # Multi-service orchestration
├── Dockerfile            # Backend container
├── requirements.txt      # Python dependencies
└── README.md

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API Endpoints

GET /

Health check endpoint.

GET /docs

Interactive API documentation (Swagger UI).

GET /incidents

List all stored incidents from database.

curl http://localhost:8000/incidents

GET /investigate/{attack_type}

Trigger investigation for a specific attack type.

Attack types:

• impossible_travel - Login from impossible geographic location
• credential_stuffing - Multiple failed login attempts
• off_hours_access - Login outside normal hours

curl http://localhost:8000/investigate/impossible_travel

WebSocket /ws

Real-time incident stream. Automatically broadcasts new incidents to connected clients.

const ws = new WebSocket('ws://localhost:8000/ws');
ws.onmessage = (event) => {
  const incident = JSON.parse(event.data);
  console.log('New incident:', incident);
};

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Example Workflow

1. Event Detected: User logs in from Moscow at 3 AM (typical location: Mumbai, 9-5)
2. Detection: Flagged as "Impossible Travel" (Isolation Forest + rule-based)
3. Investigation: ARIA retrieves context, calls Claude LLM
4. Report Generated:

   SEVERITY: CRITICAL
   CONFIDENCE: 0.9
   ATTACK PATTERN: Impossible travel detected
   MITRE TECHNIQUE: T1078 (Valid Accounts)
   
   SUMMARY:
   A critical security incident has been detected. User typically logs in 
   from Mumbai, but this login originated from Moscow. This discrepancy 
   raises concerns about potential account compromise.
   
   RECOMMENDED ACTIONS:
   1. Immediately initiate password reset
   2. Review recent account activity
   3. Notify user and request confirmation
   

5. Dashboard Update: Incident appears in real-time on React dashboard
6. Action: Security team clicks "Suspend Session" or manually investigates

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Configuration

Environment Variables

# Groq API (LLM)
GROQ_API_KEY=your_groq_api_key_here

# Database
DATABASE_URL=postgresql://user:password@host:5432/aria

# Optional
DEBUG=false
LOG_LEVEL=INFO

Detection Parameters

Edit simulator/log_generator.py to customize attack scenarios. Edit agent/investigator.py to adjust LLM prompt behavior.

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Performance

• Detection Latency: <100ms per event
• Investigation Time: 2-3 seconds (LLM API call)
• Database Query: <50ms for incident retrieval
• WebSocket Broadcast: <100ms to connected clients
• Throughput: 1,000+ events/minute on single instance

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Deployment

Deploy Backend to Render

1. Push code to GitHub
2. Go to render.com → New Web Service
3. Connect GitHub repo
4. Configure:
   • Build: pip install -r requirements.txt
   • Start: uvicorn main:app --host 0.0.0.0 --port 8000
   • Environment variables: DATABASE_URL, GROQ_API_KEY
5. Deploy

Deploy Frontend (Optional)

Dashboard can run locally while backend is on Render. Update REACT_APP_API_URL environment variable in frontend.

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What's Next (Roadmap)

• Phase 2: Integrate Isolation Forest detection to auto-flag anomalies (no more simulation)
• Phase 2: RAG + pgvector for smarter MITRE ATT&CK lookups
• Phase 3: Real log ingestion (Supabase, Auth0, Firebase integration)
• ** **: Multi-tenancy support for multiple organizations
• ** **: Slack/email alerts for critical incidents
• ** **: Custom rule engine for organization-specific detection
• Phase 4: Stripe billing integration for SaaS pricing

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Architecture Decisions

Why Groq over Claude API?

• Free tier with generous limits
• Fast inference (Llama 3.3)
• Lower latency than other providers
• Structured output support via JSON mode

Why PostgreSQL + pgvector?

• Single database for events + embeddings (future RAG)
• Open source, no vendor lock-in
• Neon provides free tier with generous limits
• Mature, battle-tested for production use

Why WebSockets?

• Sub-100ms incident delivery
• Real-time dashboard updates without polling
• Efficient for concurrent connections
• Native browser support

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Known Limitations

• Simulated Data: Currently uses generated attack scenarios. Phase 2 integrates real detection.
• Single Tenant: No multi-organization support yet.
• No Persistence: WebSocket connections don't survive server restart.
• LLM Hallucinations: Groq occasionally generates inaccurate technical details; validate critical findings.

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Contributing

This is an active portfolio project. Contributions welcome:

1. Fork the repo
2. Create feature branch (git checkout -b feature/your-feature)
3. Commit changes (git commit -m 'Add feature')
4. Push to branch (git push origin feature/your-feature)
5. Open Pull Request

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License

MIT License - see LICENSE file for details.

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Author

Ayon Ghosh
Machine Learning Engineer & Backend Developer
Building security systems one incident at a time.

• GitHub: @Ayon99
• LinkedIn: ayon-ghosh-ml
• Email: ayon2006ghosh@gmail.com

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Acknowledgments

• Groq for free LLM API tier
• Neon for free PostgreSQL hosting
• Render for free backend deployment
• Isolation Forest from scikit-learn
• MITRE ATT&CK for threat intelligence framework

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Questions? Open an issue on GitHub or reach out via email.

Want to collaborate? DM on LinkedIn or open a discussion.