Integrated Risk Management Model in Distributed It Teams

Summary. The article presents an integrated approach to project risk management in distributed IT teams, combining the experimental methodology of Chaos Engineering, probabilistic modeling based on the Monte Carlo method, and a systematic Risk Register framework. The aim of the study is to develop a scientifically grounded risk management model for distributed IT teams that incorporates the temporal dynamics of risks, their cascading interdependencies, and the adaptive update of parameters based on feedback obtained through experimentation in accordance with the Chaos Engineering methodology. Within the proposed approach, key risk parameters specific to distributed teams are formalized as time lags, communication barriers, information barriers, system stability, proxy role effectiveness, team turnover, and functional distribution. For each parameter, corresponding statistical distributions are defined, enabling accurate forecasting through simulations.
The proposed model combines quantitative risk assessment via the Monte Carlo method with data from the Risk Register and dynamic adaptation of weight coefficients depending on parameter sensitivity. This adaptation is implemented using a weighted smoothing technique that accounts for systemic feedback. The model also captures cascading risk interdependencies via a dependency matrix and integrates the Chaos Engineering approach for experimental testing of system resilience. A structured Risk Register is developed to enable systematic assessment of probability, impact magnitude, overall Risk Score, and the evaluation method for each parameter. Additionally, a sample JSON structure has been designed to facilitate digital transfer of parameters across model components.