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At least 19 records

Addressing Consequence within Operational Risk (O.T. Gagnon III) 9-18-2024

Addressing Consequence within Operational Risk: Why threats and security are just not that important! When dealing with cyber or physical risk within any critical infrastructure (CI) environment, don’t concern yourself with vulnerabilities and threats, at least not at first! Also, don’t be overly fixated on “securing the systems” within the organization. The endeavor of tackling operational risk focused on consequences in any critical infrastructure environment to include the complex Aviation ecosystem is challenging even for the most resourced entity but can be advanced though a simplified approach: identifying, binning, and prioritizing the infrastructure environment. While no two entities within a single element of the 16 critical infrastructure sectors are exactly alike when it comes to risk, there is a basic process to move toward a greater understanding of operational risk through becoming more informed about the infrastructure environment in which the entity exists. The process starts with bringing internal and external stakeholders and subject matter experts together to analyze key areas such as Information Technology (IT) and Operational Technology (OT) components and points of convergence, analyzing internal and external cyber and physical dependencies, accounting for explosive growth in devices and wireless technology, and leveraging the contributions of people inside and outside the operational environment. Attaining a common understanding of the infrastructure environment as part of addressing consequences within operational risk is not easy to do or resource light, but the process outlined provides the framework to further any entity’s efforts in this space. When it comes to cyber risks, before an organization can consider vulnerabilities within and threats to its operations, it must first have a solid understanding of the consequences existing inside its infrastructure environment. Idaho National Lab’s Consequence-Driven, Cyber-Informed Engineering is offered as an example of this approach to effective and efficient cyber risk mitigation.

99 GENERAL AND MISCELLANEOUS↗

Towards a Rigorous Basis for Specific Operations Risk Assessment of UAS

The Specific Operations Risk Assessment (SORA) guidance represents the consensus of various national aviation authorities on a common process to identify, qualitatively assess, and manage the safety risk posed by unmanned aircraft systems (UAS), when preparing the safety case required for regulatory approval to conduct certain types of operations. As such, it can be considered a de facto standard, being increasingly adopted by various relevant stakeholders. This paper first gives an overview of the SORA process and associated methods, identifying a number of inconsistencies in risk identification and assessment, also discussing plausible strategies to close the associated gaps. Then, we give a well-founded basis for the applicable concepts, such as barrier integrity, assurance, and robustness, following which we present a preliminary and simple probabilistic formalization of the underpinning barrier-based safety model. We illustrate our overall approach through a worked example, also discussing how a Bayesian framework can facilitate extending and enhancing our initial formalization. We conclude with a discussion of the opportunities afforded by our approach, such as a well-founded basis for barrier selection, whilst addressing the associated challenges. The main objective of this work is to complement the current SORA guidance through a principled, mathematicallybased approach to risk assessment, particularly when it is applied to higher-risk operational concepts that warrant greater rigor in safety assessment and assurance.

Safety Cases↗

Addressing Consequence within Operational Risk: An Approach for Understanding an Organization’s Unique Infrastructure Environment

The endeavor of tackling operational risk focused on consequences is challenging even for the most resourced entity but can be advanced though a simplified approach: identifying, binning, and prioritizing the infrastructure environment. While no two entities within a single element of the 16 critical infrastructure sectors are exactly alike when it comes to risk, there is a basic process to move toward a greater understanding of operational risk through becoming more informed about the infrastructure landscape in which the entity exists. The process starts with bringing internal and external stakeholders and subject matter experts together to analyze key areas such as Information Technology (IT) and Operational Technology (OT) components and points of convergence, analyzing internal and external cyber and physical dependencies, accounting for explosive growth in devices and wireless technology, and leveraging the contributions of people inside and outside the operational environment. Attaining a common understanding of the infrastructure landscape as part of addressing consequences within operational risk is not easy to do or resource light, but the process outlined provides the framework to further any entity’s efforts in this space.

99 GENERAL AND MISCELLANEOUS↗

Lunar Landing Operational Risk Model

Characterizing the risk of spacecraft goes beyond simply modeling equipment reliability. Some portions of the mission require complex interactions between system elements that can lead to failure without an actual hardware fault. Landing risk is currently the least characterized aspect of the Altair lunar lander and appears to result from complex temporal interactions between pilot, sensors, surface characteristics and vehicle capabilities rather than hardware failures. The Lunar Landing Operational Risk Model (LLORM) seeks to provide rapid and flexible quantitative insight into the risks driving the landing event and to gauge sensitivities of the vehicle to changes in system configuration and mission operations. The LLORM takes a Monte Carlo based approach to estimate the operational risk of the Lunar Landing Event and calculates estimates of the risk of Loss of Mission (LOM) - Abort Required and is Successful, Loss of Crew (LOC) - Vehicle Crashes or Cannot Reach Orbit, and Success. The LLORM is meant to be used during the conceptual design phase to inform decision makers transparently of the reliability impacts of design decisions, to identify areas of the design which may require additional robustness, and to aid in the development and flow-down of requirements.

Mattenberger, Chris↗

Should All Low-risk Patients Now Be Considered for TAVR? Operative Risk, Clinical, and Anatomic Considerations

Purpose of Review: This article reviews the current data on TAVR in low-risk patients with severe, symptomatic aortic stenosis, highlights the results of the recently published Medtronic Low Risk Randomized Study and PARTNER 3 trials, and describes specific clinical, anatomic, and procedural considerations regarding the optimal treatment choice in this population. Recent Findings: In low-risk patients, the Medtronic Low Risk Randomized Study demonstrated TAVR to be non-inferior to surgery with respect to the composite endpoint of death or disabling stroke while PARTNER 3 trial proved TAVR to be superior to surgery with regard to the composite endpoint of death, stroke, or rehospitalization. Summary: Recent trials demonstrate the safety and efficacy of TAVR in low-risk patients and have led to an FDA indication for the use of TAVR in these patients. However, the lack of long-term data on the rate of transcatheter valve deterioration in the younger population, higher incidence of paravalvular leak and pacemaker implantation following TAVR, along with certain intrinsic anatomic factors remain potential challenges to generalize TAVR in all low surgical risk patients. We describe specific clinical, anatomic, and procedural considerations regarding the optimal treatment choice for low-risk patients with severe, symptomatic AS.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Evidence-Based Process for Assessing Teamwork Competencies in Applicant Performance in High-Risk, Operational Occupations

In this work, we provide a detailed look at how behavioral competencies necessary for high-risk, operational jobs, including task- and person-oriented aspects of teamwork, leadership, and followership, should be defined and operationalized in context. These competencies are thought to positively predict performance in aerospace professions. Indeed, the assessments applied in high stakes selection contexts and the way associated behavioral competencies are evaluated must be carefully considered to ensure that optimal applicants are selected. We provide insight into the behavioral competency development and measurement process, elaborating upon particularly critical competencies (i.e., team orientation) and describing challenges to measurement.

Isabel Bilotta↗

The importance of operations, risk, and cost assessment to space transfer systems design

This paper examines several methodologies which contribute to comprehensive subsystem cost estimation. The example of a space-based lunar space transfer vehicle (STV) design is used to illustrate how including both primary and secondary factors into cost affects the decision of whether to use aerobraking or propulsion for earth orbit capture upon lunar return. The expected dominant cost factor in this decision is earth-to-orbit launch cost driven by STV mass. However, to quantify other significant cost factors, this cost comparison included a risk analysis to identify development and testing costs, a Taguchi design of experiments to determine a minimum mass aerobrake design, and a detailed operations analysis. As a result, the predicted cost advantage of aerobraking, while still positive, was subsequently reduced by about 30 percent compared to the simpler mass-based cost estimates.

Ball, J. M.↗

Quantification of Operational Risk Using A Data Mining

What is Data Mining? - Data Mining is the process of finding actionable information hidden in raw data. - Data Mining helps find hidden patterns, trends, and important relationships often buried in a sea of data - Typically, automated software tools based on advanced statistical analysis and data modeling technology can be utilized to automate the data mining process

Perera, J. Sebastian↗