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'Judo-BI' is a Proof-Of-Concept tool aimed at practically improving the Risk Management process, demonstrating its value in a real-world context.
Pragmatically modular, it is a Decision-Support layer that integrates with the 'C-ISMS' (Cyber-Information Security Management System) for more responsive RM; identifying patterns or critical dependencies to generate operational recommendations mapped to Security controls.

Effective Risk Management in today’s highly interconnected and fast moving/changing environments demands methodologies capable of responding in real time to emergent threats.
This preliminary draft outlines a hybrid framework that leverages advanced Machine Learning techniques, statistical modeling and quantum(-inspired) optimization — anchored by a practical use case drawn from high level Judo competitions — to develop decision making capabilities on the order of milliseconds or infinitesimal parts thereof.

By combining 3D motion capture and inertial sensing (IMUs – Inertial Measurement Units) with Reinforcement Learning (‘RL’), our system learns to recognize biomechanical patterns of imbalance and counter attack, while configurable ethical parameters (“fair play” rewards) ensure compliance with sporting or international organizational Norms.

In parallel, we integrate Artificial Intelligence into a Design Structure Matrix (‘DSM’) to dynamically reconfigure system dependencies, employ Mixed Effects Models (‘FREM’) to quantify both global and local variability, and explore quantum-inspired solvers for optimal hyperparameter tuning.

The psychological state of the athletes — captured via a composite "biorhythm" indicator (sleep quality, heart rate variability, stress level) — is included as an additional variable in feature extraction and statistical modeling.

The ultimate goal is a transferable pilot project — initially validated in the Judo domain — that simulates “attack-defense” shock scenarios in industrial and economic control processes, ensuring the RESILIENCE of a System and enabling ultra rapid, transparent, and ethically aware Risk mitigation.

Subsequent sections, currently under development, detail the data Pipeline, ‘RL’ architecture, statistical analyses, and proposed industrial mapping.