Abstract Equipment aging is one of the main challenges faced by companies operating oil and gas processing facilities with a brownfield engineering approach. As equipment ages, it breaks down more quickly, making it more susceptible to failure. The consequences of equipment failure can be disastrous, especially if the equipment is listed as a critical safeguard that protects the system from hazardous conditions. Failure of critical safeguards will increase the likelihood of hazardous scenarios occurring and thus impact the overall risk ranking of operating the facility. As a risk management effort, it is important to validate the safety integrity level (SIL) of the critical safeguards currently installed at the facilities and design an appropriate maintenance strategy to maintain the SIL value as originally designed. However, given the vast amount of equipment that is scattered over a large area, this is a very challenging effort. This paper discusses a novel attempt to validate the actual critical safeguards SIL in brownfield facilities by conducting a hybrid of an extensive reliability-centered maintenance study and a regression-based machine learning model to obtain the actual equipment failure rates based on incomplete historical failure data stored at the Computerized Maintenance Management System (CMMS). A recalculation of the current SIL level and comparing the results with the original SIL design will provide information on the level of degradation that occurred and the appropriate solution. To illustrate the application of the technique, a case study is presented based on the experience in implementing the life cycle of a safety instrumented system (SIS) according to the IEC 61508/61511 guidance as a maintenance strategy to maintain the SIL of a critical safeguard. Through these efforts, critical safeguard SILs are maintained on designed SILs and are capable of achieving credit risk reduction aimed at hazardous scenarios while setting the database for further improvement in the adoption of digital technology.