Predictive Risk Scoring by Invicti uses a dedicated machine learning model trained on real-world vulnerability data to accurately predict security risks, unlike the common use of LLMs in the industry. This approach prioritizes testing for sites efficiently, ensuring reliable and predictable results. The decision tree-based model offers fast, accurate, and interpretable risk assessments, avoiding the complexities and security risks associated with LLMs
The model's accuracy levels exceed 83% in predicting risk levels before scanning, providing users with valuable insights for prioritizing security testing. Moreover, Predictive Risk Scoring distinguishes itself from vulnerability scan results, offering a preventive outlook on potential vulnerabilities. By not relying on external AI service providers and being explainable and deterministic, Invicti's approach complies with regulations and ensures data privacy. As AI continues to evolve, especially with the potential risks posed by advanced models like llama3, the future of application security may face challenges from AI-generated cyberattacks. Enhancements to Predictive Risk Scoring are underway to incorporate additional risk factors and deliver even more robust security predictions. https://www.invicti.com/blog/web-security/predictive-risk-scoring-is-the-way-for-appsec-ai/