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

Nodes

24e3b914-15fa-444f-80e3-ca29bdacaf40f8cb31dc-fafe-49d3-8399-f520741f80ada0d93eb1-bb23-4c69-815f-fe01a740b02c818211a2-bc09-427d-bfa4-d8db9fbffb50

Created by

maMRJ

Last edited 58 days ago

: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

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

Advantage

Mitigation Strategy

Implementation Example

Rapid hazard identification

Human validation layer

Manual review nodes in workflow

Consistent S/E/C scoring

Rule-based validation

ASIL-D → Redundancy check

Edge case coverage

Cross-reference with historical data

Integration with incident databases

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

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