Amid the rapid progression of the digital transformation era, central banks are increasingly expected to not only harness emerging opportunities but also navigate and manage the growing complexity of associated risks in an effective and systematic manner. This study aims to identify and analyze optimal risk mitigation strategies within the context of digital transformation, employing a systems thinking approach and the Bayesian Network (BN) method. The risks analyzed are categorized into five main types: technological, financial, regulatory, cultural, and operational risks. We surveyed the officers in charge in IT risk mitigation to indicate the initial level of optimal risk mitigation for the high classification and medium classification as a starting point and to gain the parameters for the dynamics. Operational risk emerges as the most dominant factor influencing mitigation effectiveness, thereby underscoring the need to prioritize strong internal governance arrangements. This is followed by technological risk, which is an inseparable aspect of the digital transformation process, thus requiring the strengthening of infrastructure and cybersecurity. Scenario analysis using expert judgment can simulate an increase in optimal mitigation by strengthening six key risk nodes. Furthermore, a combination of low technological risk, enhanced system security, low third-party risk, and reduced cybersecurity vulnerabilities is shown to be the most influential set of factors driving effective mitigation. These findings underscore the importance of structured and sustainable mitigation strategies, particularly in strengthening digital security systems and operational risk management, to ensure a secure and sustainable digital transformation within central banks, or public institutions in general.

Keywords: Risk Mitigation, Central Bank, Digital Transformation, Cybersecurity, Operational Risk