# Roadmap

This document outlines the planned evolution of **AAPP-MART** as an open-source research and security simulation platform.  
The roadmap focuses on improving attack path prediction, red-team simulation capabilities, observability, and developer extensibility.

## Short Term (Next Releases)

### Core Engine Improvements

- Improve stability of the core simulation engine
- Optimize agent scheduling and task orchestration
- Improve configuration validation and error handling
- Standardize internal module interfaces
- Expand logging capabilities and structured logging

### Attack Graph & Prediction Engine

- Improved attack graph generation algorithms
- Attack path prioritization improvements
- Better hybrid prediction strategies combining:
  - rule-based analysis
  - machine learning predictors
- Path pruning and optimization algorithms
- Attack path confidence scoring

### Risk Engine Enhancements

- Expanded vulnerability scoring support
- Improved risk aggregation model
- Environmental risk factors (network exposure, privilege level, etc.)
- Better integration with the Common Vulnerability Scoring System (CVSS)

### CLI Improvements

- Interactive CLI mode
- Improved command help and documentation
- Simulation templates
- Batch scanning and automation support

## Mid Term

### Advanced Attack Simulation

- Additional MART agents:
  - cloud attack agent
  - identity attack agent
  - container attack agent
- Multi-stage attack chain simulation
- Realistic lateral movement modeling
- Privilege escalation path discovery improvements

### AI-Based Prediction Enhancements

- Graph-based machine learning predictors
- Reinforcement learning strategy tuning
- Attack strategy learning from simulation results
- Predictor benchmarking framework

### Visualization & Dashboard

- Interactive attack graph explorer
- Attack path timeline visualization
- Risk heatmap improvements
- Agent activity tracking dashboards
- Attack scenario replay

### Reporting Improvements

- Extended HTML security reports
- Executive risk summaries
- Attack path explanations
- Forensic investigation reports

## Long Term

### Research Capabilities

- Large-scale simulation environments
- Attack campaign modeling
- Autonomous red-team simulations
- AI-driven adversary behavior models
- Threat emulation based on real-world attack patterns

### Security Knowledge Integration

- Integration with public vulnerability databases
- Threat intelligence enrichment
- ATT&CK-style attack technique mapping
- Automated attack technique classification

### Plugin Ecosystem

- Plugin framework for:
  - custom agents
  - prediction engines
  - reporting modules
  - simulation modules

- Community extension marketplace
- Third-party module support

### Distributed Simulation

- Distributed attack simulations across multiple nodes
- Parallel simulation execution
- Large environment modeling
- Cluster-based simulation execution

## Developer Experience

### SDK & Extension Development

- Developer SDK for creating custom agents
- Predictor development framework
- Module testing harness
- Plugin scaffolding tools

### Testing Infrastructure

- Improved end-to-end simulation testing
- Predictor stability tests
- Scenario-based regression tests
- Continuous simulation validation

## Community & Ecosystem

### Documentation Expansion

- Advanced tutorials
- Architecture deep dives
- Research documentation
- Simulation scenario guides

### Community Collaboration

- Community attack modules
- Shared simulation scenarios
- Research collaboration support
- Contributor-driven feature development

## Future Exploration

The following areas are under research and may appear in future versions:

- autonomous attack planning
- adaptive red-team agents
- attack graph neural networks
- cyber range integration
- digital twin security simulations
- AI-driven defense modeling

## Contributing

Community contributions are welcome.

Please see:

- [CONTRIBUTING.md](/CONTRIBUTING.md)
- [CODE_OF_CONDUCT.md](/CODE_OF_CONDUCT.md)
- [SECURITY.md](/SECURITY.md)

for guidelines on contributing to the project.
