Automate ISO 26262 Compliance with GPT-4 for Automotive Safety Analysis

:car: Business Value Proposition

Accelerates ISO 26262 compliance for automotive/industrial systems by automating safety analysis while maintaining rigorous audit standards.

:gear: How It Works

graph TD
    A[Engineer uploads<br>system description] --> B(LLM identifies hazards)
    B --> C(LLM scores risks per ISO 26262)
    C --> D(Generates mitigation strategies)
    D --> E(Produces audit-ready reports)

:chart_with_upwards_trend: Key Benefits

• Time

  • 50-70% faster than manual HAZOP/FMEA sessions
  • Instant report generation vs. weeks of documentation

• Risk Mitigation

  • Pre-validated templates reduce human error
  • Auto-generated traceability simplifies audits

:warning: Governance Controls

• Human-in-the-loop: All LLM outputs require engineer sign-off
• Version tracking: Full history of modifications
• Audit mode: Export all decision rationales

:computer: Technical Requirements

• Runs on existing n8n instances
• Docker deployment (<1hr setup)
• Integrates with JAMA/DOORS (optional)

:wrench: Setup and Usage

Prerequisites

• Docker (Install Guide)
• Docker Compose (Install Guide)
• n8n instance (Free Self-Hosted or Cloud - Paid)
• OpenAI API key (Get Key)

Enterprise-ready deployment: When supported by IT infrastructure teams, this solution transforms into a scalable AI safety assistant, providing real-time HARA guidance akin to engineering Co-pilot tools.

:arrow_down: Installation and :play_or_pause_button: Running the Workflow

For installation procedures and usage of workflow, refer the repository

:warning: Validation & Limitations

AI-Assisted Analysis Considerations

Critical Validation Steps

1. AI Output Review node in n8n
   Example: (by code)

   {
     "type": "function",
     "parameters": {
       "functionCode": "if ($input.item.json.ASIL === 'D' && !$input.item.json.redundancy) throw new Error('ASIL D requires redundancy');"
     }
   }
   
   

2. Version Control

• Prompt versions tied to ISO standard editions (e.g., ISO26262:2018-v1.2)
• Git-tracked changes to ai_models/training_data/

1. Audit trails

• Providing a log structure for audit trails

# Log structure
/logs/
└── YYYY-MM-DD/
  ├── hazards_approved.log
  └── hazards_rejected.log