AI Engine

AI Engine

Z2E's AI engine (MCP-Core) powers intelligent decision-making throughout the penetration testing lifecycle.

Core Capabilities

Pattern Recognition

The AI engine identifies vulnerability patterns across:

• Code structures and common anti-patterns
• Configuration weaknesses
• Historical exploit data correlation

Adaptive Payload Generation

• Context-aware payload mutation
• Evasion technique selection
• Success probability scoring

Risk Prioritization

• ML-based severity assessment
• Business impact correlation
• Attack path optimization

MCP Protocol

The Machine Context Protocol (MCP) enables:

mcp:
  version: "2.0"
  capabilities:
    - pattern_matching
    - payload_generation
    - risk_scoring
    - evasion_planning
  
  confidence:
    minimum_threshold: 0.75
    high_confidence: 0.90

Training & Data

• Trained on curated vulnerability datasets
• Regular updates from threat intelligence feeds
• Feedback loop from successful exploitations
• Deterministic seeding for reproducibility

Next Steps