ThreatHunt/IMPLEMENTATION_SUMMARY.md

# Analyst-Assist Agent Implementation Summary

## Completed Implementation

I've successfully implemented a full analyst-assist agent for ThreatHunt following all governance principles from goose-core.

## What Was Built

### Backend (Python/FastAPI)
✅ **Agent Module** (`backend/app/agents/`)
- `core.py`: ThreatHuntAgent class with guidance logic
- `providers.py`: Pluggable LLM provider interface (local, networked, online)
- `config.py`: Environment-based configuration management

✅ **API Endpoint** (`backend/app/api/routes/agent.py`)
- POST `/api/agent/assist`: Request guidance with context
- GET `/api/agent/health`: Check agent availability

✅ **Application Structure**
- `main.py`: FastAPI application with CORS
- `requirements.txt`: Dependencies (FastAPI, Uvicorn, Pydantic)
- `run.py`: Entry point for local development

### Frontend (React/TypeScript)
✅ **Agent Chat Component** (`frontend/src/components/AgentPanel.tsx`)
- Chat-style interface for analyst questions
- Context display (dataset, host, artifact)
- Rich response formatting with pivots, filters, caveats
- Conversation history support
- Responsive design

✅ **API Integration** (`frontend/src/utils/agentApi.ts`)
- Type-safe request/response definitions
- Health check functionality
- Error handling

✅ **Main Application**
- `App.tsx`: Dashboard with agent panel in sidebar
- `App.css`: Responsive layout (desktop/mobile)
- `index.tsx`, `index.html`: React setup

✅ **Configuration**
- `package.json`: Dependencies (React 18, TypeScript)
- `tsconfig.json`: TypeScript configuration

### Docker & Deployment
✅ **Containerization**
- `Dockerfile.backend`: Python 3.11 FastAPI container
- `Dockerfile.frontend`: Node 18 React production build
- `docker-compose.yml`: Full stack with networking
- `.env.example`: Configuration template

## LLM Provider Architecture

### Three Pluggable Providers

**1. Local Provider**
```bash
THREAT_HUNT_AGENT_PROVIDER=local
THREAT_HUNT_LOCAL_MODEL_PATH=/path/to/model.gguf
```
- On-device or on-prem models
- GGML, Ollama, vLLM, etc.

**2. Networked Provider**
```bash
THREAT_HUNT_AGENT_PROVIDER=networked
THREAT_HUNT_NETWORKED_ENDPOINT=http://service:5000
THREAT_HUNT_NETWORKED_KEY=api-key
```
- Shared internal inference services
- Enterprise inference gateways

**3. Online Provider**
```bash
THREAT_HUNT_AGENT_PROVIDER=online
THREAT_HUNT_ONLINE_API_KEY=sk-key
THREAT_HUNT_ONLINE_PROVIDER=openai
THREAT_HUNT_ONLINE_MODEL=gpt-3.5-turbo
```
- OpenAI, Anthropic, Google, etc.

**Auto Selection**
```bash
THREAT_HUNT_AGENT_PROVIDER=auto
```
- Tries: local → networked → online

## Governance Compliance

✅ **Strict Policy Adherence**
- No autonomous execution (agents advise only)
- No tool execution (read-only guidance)
- No database/schema changes
- No alert escalation
- Transparent reasoning with caveats
- Analyst retains all authority

✅ **follows AGENT_POLICY.md**
- Agents guide, explain, suggest
- Agents do NOT execute, escalate, or modify data
- All output is advisory and attributable

✅ **Follows THREATHUNT_INTENT.md**
- Helps interpret artifact data
- Suggests analytical pivots and filters
- Highlights anomalies
- Assists in hypothesis formation
- Does NOT perform analysis independently

## API Specifications

### Request
```json
POST /api/agent/assist
{
  "query": "What patterns suggest suspicious activity?",
  "dataset_name": "FileList-2025-12-26",
  "artifact_type": "FileList",
  "host_identifier": "DESKTOP-ABC123",
  "data_summary": "File listing from system scan",
  "conversation_history": []
}
```

### Response
```json
{
  "guidance": "Based on the files listed, several patterns stand out...",
  "confidence": 0.8,
  "suggested_pivots": [
    "Analyze temporal patterns",
    "Cross-reference with IOCs",
    "Check for known malware signatures"
  ],
  "suggested_filters": [
    "Filter by modification time > 2025-12-20",
    "Sort by file size (largest first)",
    "Filter by file extension: .exe, .dll, .ps1"
  ],
  "caveats": "Guidance based on available data context. Verify with additional sources.",
  "reasoning": "Analysis generated based on artifact data patterns."
}
```

## Frontend Features

✅ **Chat Interface**
- Analyst asks questions
- Agent provides guidance
- Message history with timestamps

✅ **Context Awareness**
- Displays current dataset, host, artifact
- Context automatically included in requests
- Conversation history for continuity

✅ **Response Formatting**
- Main guidance text
- Clickable suggested pivots
- Suggested data filters (code format)
- Confidence scores
- Caveats section
- Reasoning explanation
- Loading and error states

✅ **Responsive Design**
- Desktop: side-by-side layout
- Tablet: adjusted spacing
- Mobile: stacked layout

## Quick Start

### Development

**Backend**:
```bash
cd backend
pip install -r requirements.txt
python run.py
# API at http://localhost:8000
# Docs at http://localhost:8000/docs
```

**Frontend**:
```bash
cd frontend
npm install
npm start
# App at http://localhost:3000
```

### Docker Deployment

```bash
# Copy and edit environment
cp .env.example .env

# Start full stack
docker-compose up -d

# Check health
curl http://localhost:8000/api/agent/health
curl http://localhost:3000

# View logs
docker-compose logs -f backend
docker-compose logs -f frontend
```

## Environment Configuration

```bash
# Provider (auto, local, networked, online)
THREAT_HUNT_AGENT_PROVIDER=auto

# Local provider
THREAT_HUNT_LOCAL_MODEL_PATH=/models/model.gguf

# Networked provider
THREAT_HUNT_NETWORKED_ENDPOINT=http://inference:5000
THREAT_HUNT_NETWORKED_KEY=api-key

# Online provider (example: OpenAI)
THREAT_HUNT_ONLINE_API_KEY=sk-your-key
THREAT_HUNT_ONLINE_PROVIDER=openai
THREAT_HUNT_ONLINE_MODEL=gpt-3.5-turbo

# Agent behavior
THREAT_HUNT_AGENT_MAX_TOKENS=1024
THREAT_HUNT_AGENT_REASONING=true
THREAT_HUNT_AGENT_HISTORY_LENGTH=10
THREAT_HUNT_AGENT_FILTER_SENSITIVE=true

# Frontend
REACT_APP_API_URL=http://localhost:8000
```

## File Structure

```
ThreatHunt/
├── backend/
│   ├── app/
│   │   ├── agents/
│   │   │   ├── __init__.py
│   │   │   ├── core.py
│   │   │   ├── providers.py
│   │   │   └── config.py
│   │   ├── api/routes/
│   │   │   ├── __init__.py
│   │   │   └── agent.py
│   │   ├── __init__.py
│   │   └── main.py
│   ├── requirements.txt
│   └── run.py
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   ├── AgentPanel.tsx
│   │   │   └── AgentPanel.css
│   │   ├── utils/
│   │   │   └── agentApi.ts
│   │   ├── App.tsx
│   │   ├── App.css
│   │   ├── index.tsx
│   ├── public/
│   │   └── index.html
│   ├── package.json
│   └── tsconfig.json
├── Dockerfile.backend
├── Dockerfile.frontend
├── docker-compose.yml
├── .env.example
├── .gitignore
├── AGENT_IMPLEMENTATION.md
├── README.md
├── ROADMAP.md
└── THREATHUNT_INTENT.md
```

## Key Design Decisions

1. **Pluggable Providers**: Support multiple LLM backends without changing application code
2. **Auto-Detection**: Smart provider selection for deployment flexibility
3. **Context-Aware**: Agent requests include dataset, host, and artifact context
4. **Read-Only**: Hard constraints prevent agent from executing, modifying, or escalating
5. **Advisory UI**: Frontend emphasizes guidance-only nature with caveats and disclaimers
6. **Conversation History**: Maintains context across multiple analyst queries
7. **Error Handling**: Graceful degradation if LLM provider unavailable
8. **Containerized**: Full Docker support for easy deployment and scaling

## Next Steps / Future Enhancements

1. **Integration Testing**: Add pytest/vitest test suites
2. **Authentication**: Add JWT/OAuth to API endpoints
3. **Rate Limiting**: Implement request throttling
4. **Structured Output**: Use LLM JSON mode or function calling
5. **Data Filtering**: Auto-filter sensitive data before LLM
6. **Caching**: Cache common agent responses
7. **Feedback Loop**: Capture guidance quality feedback from analysts
8. **Audit Trail**: Comprehensive logging and compliance reporting
9. **Fine-tuning**: Custom models for cybersecurity domain
10. **Performance**: Optimize latency and throughput

## Governance References

This implementation fully complies with:
- ✅ `goose-core/governance/AGENT_POLICY.md`
- ✅ `goose-core/governance/AI_RULES.md`
- ✅ `goose-core/governance/SCOPE.md`
- ✅ `goose-core/governance/ALERT_POLICY.md`
- ✅ `goose-core/contracts/finding.json`
- ✅ `ThreatHunt/THREATHUNT_INTENT.md`

**Core Principle**: Agents assist analysts, never act autonomously.