mblanke/ThreatHunt
1
0
Fork 0
mirror of https://github.com/mblanke/ThreatHunt.git synced 2026-03-01 05:50:21 -05:00
Code Issues Packages Projects Releases Wiki Activity
Files
main
ThreatHunt/docs/VALIDATION_CHECKLIST.md
mblanke 9b98ab9614 feat: interactive network map, IOC highlighting, AUP hunt selector, type filters 
- NetworkMap: hunt-scoped force-directed graph with click-to-inspect popover
- NetworkMap: zoom/pan (wheel, drag, buttons), viewport transform
- NetworkMap: clickable IP/Host/Domain/URL legend chips to filter node types
- NetworkMap: brighter colors, 20% smaller nodes
- DatasetViewer: IOC columns highlighted with colored headers + cell tinting
- AUPScanner: hunt dropdown replacing dataset checkboxes, auto-select all
- Rename 'Social Media (Personal)' theme to 'Social Media' with DB migration
- Fix /api/hunts timeout: Dataset.rows lazy='noload' (was selectin cascade)
- Add OS column mapping to normalizer
- Full backend services, DB models, alembic migrations, new routes
- New components: Dashboard, HuntManager, FileUpload, NetworkMap, etc.
- Docker Compose deployment with nginx reverse proxy
2026-02-19 15:41:15 -05:00

381 lines
11 KiB
Markdown
Raw Permalink Blame History

# Implementation Validation Checklist
## ✅ Completed Implementation
### Backend Agent Module
#### Core Components
- ✅ `backend/app/agents/core.py`
- ThreatHuntAgent class with guidance logic
- AgentContext and AgentResponse models
- System prompt enforcing governance
- Conversation history support
- ✅ `backend/app/agents/providers.py`
- LLMProvider abstract base class
- LocalProvider (on-device/on-prem models)
- NetworkedProvider (internal inference services)
- OnlineProvider (hosted APIs)
- get_provider() function with auto-detection
- ✅ `backend/app/agents/config.py`
- AgentConfig class with environment variable loading
- Provider-specific settings
- Behavior configuration (tokens, reasoning, history, filtering)
- is_agent_enabled() validation method
#### API Implementation
- ✅ `backend/app/api/routes/agent.py`
- POST /api/agent/assist endpoint
- GET /api/agent/health endpoint
- Request/response validation with Pydantic
- Error handling (503, 400, 500)
- Proper logging and documentation
#### Application Setup
- ✅ `backend/app/main.py` - FastAPI application with CORS
- ✅ `backend/app/__init__.py` - App module initialization
- ✅ `backend/app/api/__init__.py` - API module initialization
- ✅ `backend/app/api/routes/__init__.py` - Routes module initialization
- ✅ `backend/requirements.txt` - Python dependencies
- ✅ `backend/run.py` - Development server entry point
### Frontend Components
#### Chat Interface
- ✅ `frontend/src/components/AgentPanel.tsx`
- React component for agent chat
- Message display with timestamps
- Loading and error states
- Rich response formatting
- Suggested pivots (clickable)
- Suggested filters
- Caveats section
- Confidence scores
- Welcome message for new sessions
- Props for context (dataset, host, artifact)
- ✅ `frontend/src/components/AgentPanel.css`
- Complete styling for chat panel
- Responsive design (desktop/tablet/mobile)
- Message styling (user vs agent)
- Loading animation
- Input form styling
- Color scheme aligned with governance
#### API Communication
- ✅ `frontend/src/utils/agentApi.ts`
- Type-safe request/response interfaces
- requestAgentAssistance() function
- checkAgentHealth() function
- Proper error handling
#### Application Integration
- ✅ `frontend/src/App.tsx`
- Main application component
- Dashboard layout with agent panel
- Sample data table
- Responsive sidebar layout
- Footer with governance information
- ✅ `frontend/src/App.css`
- Dashboard styling
- Grid layout (main + sidebar)
- Table styling
- Footer layout
- Mobile responsiveness
- ✅ `frontend/src/index.tsx` - React entry point
- ✅ `frontend/src/index.css` - Global styles
- ✅ `frontend/public/index.html` - HTML template
- ✅ `frontend/package.json` - npm dependencies
- ✅ `frontend/tsconfig.json` - TypeScript configuration
### Docker & Deployment
- ✅ `Dockerfile.backend` - Python 3.11 FastAPI container
- ✅ `Dockerfile.frontend` - Node 18 React production build
- ✅ `docker-compose.yml` - Full stack orchestration
- ✅ `.env.example` - Configuration template
- ✅ `.gitignore` - Version control exclusions
### Documentation
- ✅ `AGENT_IMPLEMENTATION.md` (2,000+ lines)
- Detailed architecture overview
- Backend implementation details
- Frontend implementation details
- LLM provider architecture
- Configuration reference
- Security considerations
- Testing guide
- Troubleshooting
- ✅ `INTEGRATION_GUIDE.md` (400+ lines)
- Quick reference
- Provider configuration options
- Installation steps (Docker & local)
- Testing procedures
- Deployment checklist
- Monitoring & troubleshooting
- API reference
- Configuration reference
- ✅ `IMPLEMENTATION_SUMMARY.md` (300+ lines)
- High-level overview
- What was built
- Key design decisions
- Quick start guide
- File structure
- ✅ `README.md` - Updated with agent features
- Overview of agent-assist capability
- Quick start instructions
- Architecture overview
- Configuration guide
- API endpoints
- Governance compliance
## Governance Compliance
### ✅ AGENT_POLICY.md Adherence
- [x] Agents provide guidance, not authority
- [x] Agents do not execute tools or workflows
- [x] Agents do not escalate findings to alerts
- [x] Agents do not modify data models or contracts
- [x] Agent output is advisory and attributable
- [x] All agent interactions logged
- [x] Agents degrade gracefully if backend unavailable
- [x] Behavior consistent across applications
### ✅ AI_RULES.md Adherence
- [x] Shared concept (agent) defined in goose-core
- [x] Applications conform to shared definitions
- [x] No invented shared concepts
- [x] Agents assist analysts, never act autonomously
- [x] No execution without explicit analyst approval
### ✅ SCOPE.md Adherence
- [x] Shared concepts properly owned by goose-core
- [x] Application-specific logic in ThreatHunt
- [x] No direct database sharing
- [x] Clear responsibility boundaries
### ✅ ALERT_POLICY.md Adherence
- [x] Agent does not create alerts directly
- [x] Agent does not bypass analyst review
- [x] Agent provides guidance on findings only
### ✅ THREATHUNT_INTENT.md Adherence
- [x] Agent helps interpret artifact data
- [x] Agent suggests analytical pivots and filters
- [x] Agent highlights anomalies and patterns
- [x] Agent assists in hypothesis formation
- [x] Agent does NOT perform analysis independently
## Technical Architecture
### ✅ Pluggable LLM Provider Pattern
- [x] Abstract LLMProvider base class
- [x] LocalProvider implementation
- [x] NetworkedProvider implementation
- [x] OnlineProvider implementation
- [x] Auto-detection mechanism
- [x] Graceful degradation
### ✅ Request/Response Contract
- [x] AgentContext with structured fields
- [x] AgentResponse with comprehensive fields
- [x] Pydantic validation
- [x] Type-safe API communication
### ✅ Governance Enforcement
- [x] System prompt restricting agent behavior
- [x] Read-only guidance only
- [x] No database access
- [x] No alert escalation
- [x] Transparent reasoning with caveats
- [x] Confidence scoring
### ✅ Frontend Design
- [x] Chat interface for natural interaction
- [x] Context awareness (dataset, host, artifact)
- [x] Rich response formatting
- [x] Conversation history
- [x] Loading and error states
- [x] Responsive design
- [x] Advisory disclaimers
## Configuration & Deployment
### ✅ Environment Configuration
- [x] Provider selection via env var
- [x] Provider-specific configuration
- [x] Behavior settings (tokens, reasoning, etc.)
- [x] Privacy settings (sensitive data filtering)
- [x] Auto-detection logic
### ✅ Docker Support
- [x] Backend Dockerfile
- [x] Frontend Dockerfile (multi-stage)
- [x] docker-compose.yml with networking
- [x] Health checks
- [x] Non-root users
- [x] Volume configuration options
### ✅ Development Support
- [x] requirements.txt with optional dependencies
- [x] package.json with react/typescript
- [x] Local development entry point (run.py)
- [x] .env.example template
- [x] .gitignore for version control
## Testing & Documentation
### ✅ Documentation
- [x] Comprehensive technical guide (AGENT_IMPLEMENTATION.md)
- [x] Quick start guide (INTEGRATION_GUIDE.md)
- [x] API documentation (inline comments)
- [x] Configuration reference
- [x] Troubleshooting guide
- [x] Security notes for production
- [x] Usage examples
### ✅ Testability
- [x] Health check endpoint
- [x] Example curl commands documented
- [x] UI for manual testing
- [x] Environment variable configuration
- [x] Error handling and logging
## Files Created (25 total)
### Backend (10 files)
1. backend/app/agents/__init__.py
2. backend/app/agents/core.py
3. backend/app/agents/providers.py
4. backend/app/agents/config.py
5. backend/app/api/__init__.py
6. backend/app/api/routes/__init__.py
7. backend/app/api/routes/agent.py
8. backend/app/__init__.py
9. backend/app/main.py
10. backend/requirements.txt
11. backend/run.py
### Frontend (8 files)
12. frontend/src/components/AgentPanel.tsx
13. frontend/src/components/AgentPanel.css
14. frontend/src/utils/agentApi.ts
15. frontend/src/App.tsx
16. frontend/src/App.css
17. frontend/src/index.tsx
18. frontend/src/index.css
19. frontend/public/index.html
20. frontend/package.json
21. frontend/tsconfig.json
### Deployment (4 files)
22. Dockerfile.backend
23. Dockerfile.frontend
24. docker-compose.yml
25. .env.example
### Documentation (4 files)
26. AGENT_IMPLEMENTATION.md
27. INTEGRATION_GUIDE.md
28. IMPLEMENTATION_SUMMARY.md
29. .gitignore
30. README.md (updated)
## Key Features
### ✅ Read-Only Guidance
- Agent analyzes data context
- Suggests analytical directions
- Proposes filters and pivots
- Highlights patterns and anomalies
- NO execution, NO escalation, NO modification
### ✅ Context-Aware
- Accepts dataset name
- Accepts artifact type
- Accepts host identifier
- Includes data summary
- Maintains conversation history
### ✅ Transparent Reasoning
- Main guidance text
- Suggested pivots (2-4 suggestions)
- Suggested filters (2-4 suggestions)
- Confidence scores
- Caveats and limitations
- Reasoning explanation
### ✅ Flexible Deployment
- Local models (privacy-first)
- Networked services (enterprise)
- Online APIs (convenience)
- Auto-detection (flexibility)
### ✅ Production-Ready
- Error handling
- Health checks
- Logging
- CORS configuration
- Non-root containers
- Configuration management
## Success Criteria
✅ **Backend**
- [x] Pluggable LLM provider interface implemented
- [x] FastAPI endpoint created (/api/agent/assist)
- [x] Configuration management working
- [x] Read-only governance enforced
✅ **Frontend**
- [x] Chat panel component created
- [x] Context-aware (dataset, host, artifact)
- [x] Response display with pivots, filters, caveats
- [x] Integrated into main app with sidebar
✅ **Governance**
- [x] No execution capability
- [x] No database changes
- [x] No alert escalation
- [x] Follows AGENT_POLICY.md
- [x] Follows THREATHUNT_INTENT.md
✅ **Documentation**
- [x] Technical architecture documented
- [x] Configuration options documented
- [x] Quick start guide provided
- [x] Troubleshooting guide included
- [x] API reference documented
✅ **Deployment**
- [x] Docker support complete
- [x] Environment configuration flexible
- [x] Health checks implemented
- [x] Multi-provider support ready
## Next Steps for User
1. **Configure Provider**: Set environment variables in .env
2. **Start Services**: Run `docker-compose up -d`
3. **Verify**: Check health at `http://localhost:8000/api/agent/health`
4. **Test**: Open `http://localhost:3000` and ask agent a question
5. **Integrate**: Add to your threat hunting workflow
6. **Monitor**: Track agent usage and feedback quality
## Notes
- All code follows governance principles from goose-core
- Agent provides **advisory guidance only**
- **No autonomous actions** or execution
- **Analyst retains all authority** over decisions
- Implementation is **production-ready** with proper error handling
- Documentation is comprehensive and actionable
Reference in New Issue View Git Blame Copy Permalink