Risk management is now available in Davinci! As engineering systems grow more complex and development cycles accelerate, teams face mounting pressure to identify and mitigate risks early without slowing down delivery. Traditional risk management processes struggle to keep pace with modern engineering demands, often becoming bottlenecks rather than enablers. The core capability is straightforward: AI-assisted risk identification and classification that works across any engineering field, helping teams maintain comprehensive risk coverage at the speed their projects require.

The primary advantage is scale and speed. Traditional risk identification involves whiteboard sessions, manual documentation, and iterative reviews, a process that typically consumes hours or days of team time. Davinci can help enhance this by generating and analyzing dozens or hundreds of potential risks in minutes. The AI works by analyzing system relationships within your model, cross-referencing component interactions, and applying pattern recognition from engineering failure modes to help identify risks you might otherwise miss. For aerospace teams evaluating failure modes across propulsion systems, this can help identify risks such as turbine blade fatigue, fuel system contamination, or thrust vector control failures. Cybersecurity teams can use this to help identify potential vulnerabilities across multiple system layers, including authentication bypass risks, data exposure through API endpoints, or encryption implementation weaknesses. Similarly, automotive teams can use this to assist in evaluating risks like battery thermal runaway, sensor degradation under environmental stress, or brake system hydraulic failures at a pace that supports modern development cycles.

What makes this practical is integration. Risk objects integrate directly into your system model, connected to parts, requirements, or any other object. This eliminates the disconnect between risk registers and design artifacts. When you modify a design element, associated risks are immediately visible in context. AI agents can query your model to surface relevant risks as your architecture evolves, ensuring risk analysis stays synchronized with your actual design rather than becoming a separate document that falls out of date.

Each risk is scored across three dimensions: Probability, Impact, and Detectability. Risk matrices are customizable to match your project requirements and industry standards, whether you’re working with FMEA, ISO 31000, or internal methodologies.

The bottom line is efficiency and better resource allocation. Engineering teams can identify and classify risks faster, reducing the time spent on manual documentation and freeing up bandwidth for what matters: developing mitigation strategies, refining design solutions, and running validation tests. Instead of spending days compiling risk registers, teams can generate comprehensive risk analysis in minutes and immediately move to addressing the issues that matter. This is practical tooling that perfectly shows how Davinci is helping teams build the future.