The software industry has witnessed a growing interest in DevSecOps due to the premises of integrating security in the software development lifecycle. However, security compliance cannot be disregarded, given the importance of adherence to regulations, laws, industry standards, and frameworks. This study aims to provide an overview of compliance aspects in the context of DevSecOps and explore how compliance is ensured. Furthermore, this study reveals the trends of compliance according to the extant literature and identifies potential directions for further research in this context. Therefore, we carried out a systematic literature review on the integration of compliance aspects in DevSecOps, which rigorously followed the guidelines proposed by Kitchenham and Charters. We found 934 articles related to the topic by searching five bibliographic databases (163) and Google Scholar (771). Through a rigorous selection process, we selected 15 papers as primary studies. Then, we identified the compliance aspects of DevSecOps and grouped them into three main categories: compliance initiation, compliance management, and compliance technicalities. We observed a low number of studies; therefore, we encourage further efforts into the exploration of compliance aspects, their automated integration, and the development of metrics to evaluate such a process in the context of DevSecOps.

The growing complexity of global financial systems, combined with the digital transformation driven by AI and decentralized technologies, has increased vulnerabilities in data security, privacy, and regulatory compliance. These challenges are particularly dire in the financial sector, where institutions must not only safeguard sensitive financial data but also comply with diverse national and international regulatory frameworks. This study aims to investigate existing approaches for securing financial systems through machine learning, legal frameworks, and regulations, while also highlighting strategies based on decentralization. The review is conducted through the database searches of relevant literature in IEEE Xplore, ACM, ScienceDirect, Springer, and Web of Science. The search covers journals and conferences from 2014 to 2024. Studies not in English and those not addressing the security of financial systems through data sovereignty are excluded. We adopted PRISMA for selecting the final papers for analysis. A total of 741 studies were identified and narrowed down to 52 studies. Following a comprehensive analysis of the legal frameworks and regulatory measures pertaining to artificial intelligence (AI) across various nations and regions, it emerges that the European Union (EU) is at the forefront in this domain. Our in-depth research reveals significant variations in the methodologies employed by different countries to regulate AI and maintain sovereignty over financial data. Of the 13 data sovereignty methods examined, only five explicitly address security concerns, which are viewed as the most important component. Research highlights the challenges of protecting financial systems through data sovereignty in an increasingly digital economy. Although regulatory frameworks, particularly in the EU, have made significant progress in addressing AI-related risks in banking, challenges remain in balancing innovation with regulatory compliance across jurisdictions. The research emphasizes that while AI and machine learning technologies offer significant potential for enhancing financial security, they must operate within robust, harmonized regulatory frameworks to address issues of trust, privacy, and autonomy. Future research should focus on harmonizing global regulatory efforts and exploring ethical considerations to ensure financial systems remain secure, compliant, and inclusive.

Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems (ICS). These systems are becoming more connected to the internet, either directly or through the corporate networks. This makes them vulnerable to cyber-attacks. Abnormal behaviour in floodgates operated by ICS could be caused by both (intentional) attacks and (accidental) technical failures. When operators notice abnormal behaviour, they should be able to distinguish between those two causes to take appropriate measures, because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector. In the previous work, we developed the attack-failure distinguisher framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two causes, including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models. As a full case study of the attack-failure distinguisher framework, this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates, addressing the problem of floodgate operators. We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model. The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements. This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements.

The ongoing decade was believed to be a peaceful one. However, contemporary conflicts, and in particular, ongoing wars prove the opposite as they show the increase in context complexity when defining their goals as well as execution strategies for building means and methods for achieving them by gaining advantage against their adversaries through the engagement of well-established targets. At the core of the engagement decision relies the principle of proportionality which brings in a direct relation the expected unintended effects on civilian side with the anticipated intended effects on military side. While the clusters of effects involved in the proportionality assessment are clear, the process itself is subjective, governed by different dimensions of uncertainty, and represents the responsibility of military Commanders. Thus, a complex socio-technical process where different clusters of influential factors (e.g., military, technical, socio-ethical) play a role in the decisions made. Having said that, the objective of this research is to capture and cluster these factors, and further to model their influence in the proportionality decision-making process. This decision support system produces military targeting awareness to the agents involved in the processes of building, executing, and assessing military operations. To accomplish the aim of this research, a Design Science Research methodological approach is taken for capturing and modelling the influential factors as a socio-technical artefact in the form of a Bayesian Belief Network (BBN) model. The model proposed is further evaluated through demonstration on three different cases in respect to real military operations incidents and scenarios existing in the scientific literature in this research field. Hence, through this demonstration, it is illustrated and interpreted how the factors identified influence proportionality decisions when assessing target engagement as being proportional or disproportional. In these cases, corresponding measures for strengthening proportionality and reducing disproportionality in military operations are considered.