Automating Threat Modeling with Gen AI and LLM

Leveraging Generative AI and Large Language Models (LLMs) for automating threat modeling is an innovative approach that can significantly enhance the efficiency and effectiveness of security practices in software development. Your research and ideas align well with current trends in application security and DevSecOps. Here's a refined overview of your proposal:

Shift-Left Security

The "Shift-Left" approach emphasizes integrating security earlier in the Software Development Lifecycle (SDLC). This proactive strategy ensures security is considered from the inception and planning phases, promoting secure-by-design principles

Scaling and Automation

Building on existing automated DevOps practices (SAST/DAST/IAST/SCA), the proposal aims to scale threat modeling using Generative AI and LLMs. This aligns with the industry trend of enhancing security processes through automation and scalability

Key Enhancements

1. Agile Threat Modeling: Integrating threat modeling into DevOps pipelines for continuous security assessment.

2. Framework Flexibility: Utilizing various security frameworks (OWASP, NIST, Databricks) based on specific needs, including AI and data pipelines.

3. Time Efficiency: Reducing manual effort in repetitive threat modeling tasks.

Proposed Implementation

1. Input Processing: Leverage Velo documentation and diagrams as input for the AI model.

2. AI Model Customization: Train the model on Sportsbet security objectives and relevant frameworks (NIST, CIS, OWASP Top 10, Databricks AI Security) to reduce false positives.

3. Output Generation: Produce HTML reports containing:

   • Solution diagram with components and interfaces

   • STRIDE Framework analysis for each component

   • Threat assessment and required pre-launch tests

Potential Benefits

• Accelerated threat modeling process

• Consistent application of security frameworks

• Improved integration with existing DevOps practices

• Enhanced scalability of security assessments

Considerations and Recommendations

1. Model Training: Ensure the AI model is regularly updated with the latest security knowledge and company-specific requirements.

2. Human Oversight: Maintain human expertise in the loop to validate AI-generated assessments and handle complex scenarios.

3. Continuous Improvement: Implement feedback mechanisms to refine the AI model's accuracy over time.

4. Integration: Seamlessly integrate the automated threat modeling tool into existing development workflows.

This approach to automating threat modelling with AI aligns well with modern DevSecOps practices and has the potential to significantly enhance security efforts while reducing manual workload

At last, can not complete this article without saying thanks and mentioning about Abhay Bhargav and his webinar https://www.youtube.com/watch?v=ZNWptwfa0DE.
He is great and I am learning a lot from appsecengineer.com webinars, please add this in your podcast list if you want to learn and uplift your knowledge about Application security.