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In the current ecosystem of cyber-physical systems and IoT, the ability to interpret why a system behaves in a certain way has become a critical challenge. While traditional explainability methods focus on correlations between input and output variables, causal analysis seeks to answer interventional questions: what would happen if a component were modified? However, reconstructing an explicit causal graph in large-scale hybrid systems with feedback loops and partial observability is often impractical. This is where an innovative approach inspired by statistical mechanics emerges: modeling dependencies between variables through an undirected energy representation. Instead of seeking direct causal relationships, it analyzes how the system's energy landscape varies under perturbations, enabling robust and scalable attribution. This method, which could be called 'physical structural attribution,' offers a valuable alternative for industrial cybersecurity, anomaly detection, and diagnostics in IoT environments.
The transition from graphs to gradients represents a paradigm shift. Energy gradients capture the influence of each component without needing an explicit causal graph, facilitating human interpretation and integration with subsequent predictive tasks. In practical applications, such as monitoring an industrial plant with hybrid sensors (continuous and discrete), this technique has demonstrated greater attribution accuracy, better robustness against noise, and superior scalability compared to graph-based approaches.
For companies developing technological solutions in this field, having advanced explainability tools is essential. At Q2BSTUDIO, we offer cybersecurity services that integrate vulnerability analysis and IoT system monitoring, along with consulting in artificial intelligence for businesses. Our experience in developing custom applications and custom software allows us to implement architectures that incorporate structural attribution methods, improving transparency and trust in autonomous systems.
Additionally, we combine these capabilities with AWS and Azure cloud services, ensuring scalable deployment of AI models. For data visualization and decision-making, we apply business intelligence services with Power BI, enabling our clients to understand their systems' behavior in real time. We also develop AI agents that automate anomaly detection and incident response, all under a robust explainability approach.
Ultimately, physical structural attribution opens new avenues for understanding complex systems without relying on difficult-to-obtain causal graphs. At Q2BSTUDIO, we are ready to help companies adopt these methodologies, offering solutions ranging from consulting to the complete development of secure and explainable IoT platforms.
