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

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

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

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

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

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

{
  "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:

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

Frontend:

cd frontend
npm install
npm start
# App at http://localhost:3000

Docker Deployment

# 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

# 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.