MRJ
Workflows by MRJ
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** ```mermaid 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](https://docs.docker.com/engine/install/)) - Docker Compose ([Install Guide](https://docs.docker.com/compose/install/)) - n8n instance ([Free Self-Hosted](https://docs.n8n.io/hosting/installation/server-setups/docker-compose/) or [Cloud - Paid](https://n8n.io/cloud/)) - OpenAI API key ([Get Key](https://platform.openai.com/api-keys)) 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](https://github.com/ManiRajan1/Project_repositories/blob/AI_Assisted_HARA/docs/README.AI_Assisted_HARA.md) ## :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) ```json { "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/ 3. **Audit trails** + Providing a log structure for audit trails ``` bash # Log structure /logs/ └── YYYY-MM-DD/ ├── hazards_approved.log └── hazards_rejected.log ```
Automated hazard analysis for ISO 26262 compliance using GPT-4
**Modular Hazard Analysis Workflow : Free Version** ## **Business Value Proposition** **Accelerates ISO 26262 compliance** for automotive/industrial systems by automating safety analysis while maintaining rigorous audit standards. ## :chart_with_upwards_trend: **Key Benefits** + Time - Instant report generation vs. weeks of documentation for HAZOP + Risk Mitigation - Pre-validated templates reduce human error ### Quick guide - Input a systems_description file to the workflow - Provide an OPENAI_API_KEY to the chat model. You can also replace the chat model with the model of your interest. ### :play_or_pause_button: Running the Workflow Refer to the [github repo](https://github.com/ManiRajan1/Project_repositories/blob/AI_Assisted_HARA/docs/README.AI_Assisted_HARA.md) to understand in detail about how the workflow can be used ### :email: Contact For collaboration proposals or security issues, contact me by [Email]([email protected]). ## :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 |