# Features

**AAPP‑MART** (AI-Autonomous Attack Path Prediction & Multi‑Agent Red Team Simulation Engine) is an AI‑powered offensive‑security engine designed to model attacker behavior, predict attack paths, and validate defensive controls through autonomous simulations. This document provides a detailed overview of the platform’s core capabilities.

## Core Capabilities

### **AI‑Autonomous Attack Path Prediction**
- Uses machine learning models to estimate likely attacker movement.
- Generates dynamic attack paths based on environment state and security posture.
- Supports probabilistic and deterministic prediction modes.

### **Multi‑Agent Red Team Simulation Engine**
- Autonomous adversarial agents emulate realistic attacker tactics.
- Agents coordinate, compete, or specialize based on scenario configuration.
- Behavior aligned with MITRE ATT&CK techniques.

### **CORE Orchestration Engine**
- Coordinates the interaction between AAPP and MART to maintain a unified simulation flow.  
- Manages the global environment state, event propagation, and scenario progression.  
- Controls agent decision cycles, execution timing, and system‑wide synchronization.

## Intelligence & Analytics

### **Predictive Analytics Engine**
- Identifies high‑risk nodes, misconfigurations, and privilege‑escalation paths.
- Computes attack feasibility scores and risk impact metrics.
- Generates actionable insights for defenders.

### **Attack Graph Generation**
- Builds dynamic attack graphs from environment data.
- Visualizes relationships between assets, vulnerabilities, and attack vectors.
- Supports export for external analysis tools.

## Security Validation

### **Continuous Security Assessment**
- Runs scheduled or on‑demand simulations.
- Detects drift in security posture over time.
- Validates defensive controls before real attackers exploit them.

### **Controlled Simulation Environment**
- Safe, isolated execution of adversarial behavior.
- Configurable constraints for risk‑free testing.
- Supports cloud, hybrid, and on‑prem environments.

## Architecture & Extensibility

### **Modular Architecture**
- Pluggable components for agents, models, and environment adapters.
- Easy integration with external tools and data sources.

### **Scenario‑Based Simulation**
- Customizable attack scenarios.
- Supports training, testing, and research workflows.

### **API‑Driven Design**
- Python API for automation and scripting.
- Extensible interfaces for custom agents and models.

## Output & Reporting

### **Simulation Reports**
- Detailed logs of agent actions and attack sequences.
- Risk scoring and impact summaries.
- Exportable JSON/Markdown reports.

## Target Users

- CISOs, InfoSec managers, and executive stakeholders seeking actionable security intelligence  
- Security, engineering, and risk teams aiming to proactively assess and improve cyber resilience  
- Internal/External red, blue, and purple teams requiring realistic, repeatable adversary emulation  
- Organizations subject to regulatory or compliance mandates (MITRE ATT&CK, NIST, CIS, PCI DSS, ISO 27001, etc.)
