# A Resilience Engineering Approach for the Risk Assessment of IT Services
## 1 Introduction  
Recently, the concept of resilience has been applied to numerous different contexts to stress the role of adaptability and variability in dealing with the events that are characterizing our lives in these years, such as the COVID-19 pandemic, the energy crisis, wars, climate changes, etc. [1; 2; 3; 4]. As stressed by [PERSON] et al. [5], factors such as complexity, ambiguity, constrained resources, and uncertainty are more and more shaping our lives, requiring adaptability and adaptive behaviors to face the variety of perturbations that might occur in real systems. While at a general level, the definition of resilience proposed by the United Nations can illustrate its broad application in the case of adverse situations (\"The ability of a system, community or society exposed to hazards to resist, absorb, accommodate to, and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions\") [6], in the engineering context the research approach proposed by [PERSON] et al. [7] can be considered as one of the most accepted and diffused, describing resilience as \"The intrinsic ability of a system to adjust its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations under both expected and unexpected conditions\". Resilience represents a key factor in enhancing risk assessment, allowing the shift from traditional risk analysis, mainly based on a control-centric approach, to a more modern concept, which is usually called \"Safety II\" and relies on the acknowledgment that both acceptable and adverse outcomes are based on everyday performance adjustments [8]. Actually, as stressed by [PERSON] et al. [9], traditional risk analysis tools belonging to the \"Safety I\" approach, such as Failure Modes and Effect Analysis (FMEA), Fault Tree Analysis (FTA), and Event Tree Analysis (ETA), provide a bimodal perspective of work activities, according to which positive or negative outcomes are a consequence of different systems' modes of functioning. Hence, despite the advantages in terms of usability, Safety I tools fail to capture the complexity and variability of human performances and related activities [10]. Accordingly, failures should be considered a result of the everyday variability of human performances rather than unique events. Hence, adverse outcomes are not only due to failures and malfunctions but also to the result of performance variabilities [11]. In order to put into practice this novel approach, the Functional Resonance Analysis Method (FRAM) is one of the most widespread tools for modeling causal factors of accidents (i.e., what goes wrong) and the behavior of sociotechnical systems [12]. This method is currently considered one of the few structured \"Safety II\" tools used in the industrial context [13]. As pointed out by [PERSON] [14], FRAM allows safety managers to describe the functions of a complex system by characterizing their potential variability and the functional resonance based on dependencies and couplings among them. More in detail, FRAM is aimed at analyzing how a certain socio-technical system works by describing it not in terms of components but as a set of functions that represent the \"work-as-done\" [15]. In this way, it is possible to continuously adjust the system's performance, allowing things to go right; conversely, when the performance variability leads to unexpected outcomes, which are described as functional resonance, things go wrong and accidents may occur [16]. In recent literature, numerous studies have investigated the implementation of FRAM to enhance resilience in different domains [17; 18; 19]. [PERSON] et al. [20] observed that the reduction of unplanned chains of events and losses can be achieved by a proactive management of risks, which can enhance the quality of performance and productivity.