---
title: Security Incident Analyzer
emoji: 🛡️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.2.0
app_file: src/app.py
pinned: false
license: mit
---

# Security Incident Analyzer

An LLM-powered security log and incident analysis tool with a production-ready web interface.

## 🎯 What It Does

Paste security logs or alerts → Get immediate AI analysis explaining:
- **What happened** — Clear incident summary
- **Severity level** — CRITICAL, HIGH, MEDIUM, LOW, or INFO
- **Suggested remediation** — Actionable next steps
- **Key indicators** — IOCs and anomalies detected

No training required. Director-level insights in seconds.

## 🚀 Try It Now

**Live Demo:** https://huggingface.co/spaces/debashis2007/SecurityIncidentAnalyzer

Or run locally (5 minutes):

```bash
git clone https://github.com/Debashis2007/SecurityIncidentAnalyzer.git
cd SecurityIncidentAnalyzer
pip install -r requirements.txt
cp .env.example .env
python src/app.py  # Visit http://localhost:7860
```

## 🏗️ Architecture

```
src/
├── app.py                    # Gradio web UI entry point
├── analyzer/
│   ├── security.py          # IncidentAnalyzer: core analysis logic
│   └── models.py            # RiskLevel enum, SecurityAnalysis dataclass
├── llm/
│   ├── provider.py          # LLM provider abstraction
│   │                        # - OpenAIProvider (gpt-4-turbo)
│   │                        # - LocalLLMProvider (Ollama)
│   │                        # - MockLLMProvider (demo)
│   └── prompts.py           # Security analysis prompt templates
└── utils/
    ├── config.py            # Environment-driven configuration
    └── logger.py            # Structured logging
```

## ⚙️ Configuration

### Option 1: Mock LLM (Default - No API Key Required)

Perfect for demos and testing:

```bash
# No setup needed! Just run:
python src/app.py
```

The mock provider returns realistic-looking security analysis without any API calls.

### Option 2: OpenAI GPT-4 Turbo

For production analysis with real LLM intelligence:

1. **Get an API key:**
   - Sign up: https://platform.openai.com
   - Create key: https://platform.openai.com/account/api-keys
   - Keep it safe!

2. **Configure locally:**
   ```bash
   cp .env.example .env
   # Edit .env:
   # LLM_PROVIDER=openai
   # OPENAI_API_KEY=sk-proj-YOUR_KEY_HERE
   python src/app.py
   ```

3. **On Hugging Face Spaces:**
   - Settings → Secrets → Add:
     - `OPENAI_API_KEY` = your key
     - `LLM_PROVIDER` = openai

### Option 3: Local LLM (Ollama)

Run a local model without cloud API:

1. **Install Ollama:** https://ollama.ai
2. **Pull a model:**
   ```bash
   ollama pull mistral:7b
   ollama serve  # Keep running in another terminal
   ```

3. **Configure:**
   ```bash
   # .env
   LLM_PROVIDER=local
   LLM_MODEL=mistral:7b
   ```

## 📋 Environment Variables

Create a `.env` file (see `.env.example`):

```bash
# Provider selection (required)
LLM_PROVIDER=mock          # mock, openai, or local

# OpenAI (only needed if LLM_PROVIDER=openai)
OPENAI_API_KEY=sk-proj-...

# Model override (optional, uses defaults if not set)
LLM_MODEL=gpt-4-turbo

# Debugging
DEBUG=false
```

**⚠️ Important:** `.env` is in `.gitignore` — secrets never get committed.

### Provider Defaults

| Provider | Default Model | Notes |
|----------|---------------|-------|
| `mock` | mock-analyzer-v1 | No API required, deterministic |
| `openai` | gpt-4-turbo | Requires OPENAI_API_KEY |
| `local` | mistral:7b | Requires Ollama running on localhost:11434 |

## 🧪 Testing

```bash
# Run all tests
pytest tests/ -v

# With coverage report
pytest tests/ --cov=src

# Test specific module
pytest tests/test_analyzer.py -v
```

**Test Results:** 11/11 tests passing ✅

## 📝 Example Incidents to Test

### 1. Failed Authentication
```
2025-12-21 14:32:15 AUTH_FAILURE - Failed login attempt from 192.168.1.100
2025-12-21 14:32:18 AUTH_FAILURE - Failed login attempt from 192.168.1.100
2025-12-21 14:32:21 AUTH_FAILURE - Failed login attempt from 192.168.1.100
User: admin@company.com | Attempts: 15 in 2 minutes
```

### 2. Ransomware Detection
```
CRITICAL ALERT - File encryption detected
Directory: /mnt/backup/production
Files Encrypted: 500+
Process: unknown.exe (SYSTEM privilege)
Time: 2025-12-21 16:20:15 UTC
Status: ACTIVE THREAT
```

### 3. SQL Injection Attempt
```
Web Application Firewall Alert
Rule: SQL Injection Pattern
URL: /api/users?id=1' OR '1'='1
Source IP: 203.0.113.45
Status: BLOCKED
Payload: ' OR '1'='1
```

### 4. Privilege Escalation
```
Security Event: Privilege Escalation Detected
User: john.smith@company.com
Action: sudo su - (unauthorized)
Target System: prod-db-server-02
Time: 2025-12-21 17:02:45
Status: No approval ticket found
```

### 5. Suspicious Outbound Traffic
```
ALERT: Unusual outbound traffic detected
Destination: 10.0.0.1:4444
Source: internal-server-03
Data Volume: 2.3 GB in 45 minutes
Status: ONGOING
```

## 🚀 Deployment

### Hugging Face Spaces (Recommended)

Your app is live at:
👉 https://huggingface.co/spaces/debashis2007/SecurityIncidentAnalyzer

**How it works:**
- Detects `spaces.yaml` → Gradio configuration
- Installs `requirements.txt` → All dependencies
- Runs `src/app.py` → Your app starts automatically

**To add OpenAI:**
1. Settings → Secrets
2. Add `OPENAI_API_KEY=sk-proj-YOUR_KEY`
3. Change `.env`: `LLM_PROVIDER=openai`
4. Push to git (auto-redeploys)

### Docker (Local or Cloud)

```dockerfile
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
ENV LLM_PROVIDER=mock
CMD python src/app.py
```

```bash
docker build -t security-analyzer .
docker run -p 7860:7860 security-analyzer
```

### Traditional Server

```bash
# Production with gunicorn
pip install gunicorn
gunicorn -w 4 -b 0.0.0.0:7860 src.app:demo
```

## 🔧 For AI Developers

See `.github/copilot-instructions.md` for comprehensive agent onboarding:
- **Architecture patterns** — Provider abstraction, config management, regex parsing
- **Data flows** — How logs become analyzed incidents
- **Extension points** — Add new LLM providers, customize analysis
- **Testing strategy** — Unit, integration, E2E with mocks
- **Troubleshooting** — Common issues and solutions

## 📦 Requirements

- **Python:** 3.9+
- **Core:** Gradio, Pydantic, httpx, python-dotenv
- **Optional:** OpenAI SDK (for OpenAI provider only)

See `requirements.txt` for exact versions.

## 🧠 Key Design Decisions

1. **Provider Abstraction** — LLM logic decoupled from analysis
2. **Environment Config** — All settings via env vars, no hardcoding
3. **Regex Parsing** — Flexible response extraction (works with any LLM format)
4. **Async/Await** — Non-blocking I/O for responsive UI
5. **Structured Types** — Pydantic models for validation and safety

## ⚖️ License

MIT — Use freely, modify, distribute.

## 🤝 Contributing

```bash
# Fork, create a branch, make changes
git checkout -b feature/my-improvement
pytest tests/ -v  # Ensure tests pass
git push origin feature/my-improvement
# Create a Pull Request
```

## ❓ FAQ

**Q: Can I use this without an API key?**
A: Yes! The default `mock` provider needs no API key and is perfect for demos.

**Q: How accurate is the analysis?**
A: Depends on the LLM. Mock is deterministic for testing. OpenAI (GPT-4) provides production-grade analysis.

**Q: Can I run this offline?**
A: Yes! Use the `local` provider with Ollama on your own hardware.

**Q: Is my data safe?**
A: - **Mock provider:** No external calls, stays local
- **Local provider:** Stays on your machine
- **OpenAI provider:** Subject to OpenAI's privacy policy

**Q: How do I report bugs?**
A: Open an issue on GitHub with reproduction steps.

---

**Questions?** Check `.github/copilot-instructions.md` or create an issue!

## Contributing

Pull requests welcome! Please ensure tests pass:
```bash
pytest tests/ -v
```