Financial institutions face increasing cyber risk while operating under strict regulatory oversight. To manage this risk, they rely heavily on Cyber Threat Intelligence (CTI) to inform detection, response, and strategic security decisions. Artificial intelligence (AI) is widely suggested as a means to strengthen CTI. However, evidence of trustworthy production use in finance remains limited. Adoption depends not only on predictive performance, but also on governance, integration into security workflows and analyst trust. Thus, we examine how AI is used for CTI in practice within financial institutions and what barriers prevent trustworthy deployment.

• The SAFT-GT toolchain enables semi-automatic Attack-Fault Tree generation for enhanced safety and security assessment in self-adaptive systems. • The toolchain efficiently integrates into the feedback loop of self-adaptive systems, allowing for dynamic updates based on security assessments. • A user study with domain experts confirms the toolchain’s relevance and practical applicability in real-world scenarios. • Performance experiments demonstrate that the Attack-Fault Tree generation pipeline operates within feasible time constraints, supporting real-time applications. • The complete toolchain and resources are provided for download, fostering further research and collaboration in the field. In the rapidly evolving landscape of software engineering, the demand for robust and secure systems has become increasingly critical. This is especially true for self-adaptive systems due to their complexity and the dynamic environments in which they operate. To address this issue, we designed and developed the SAFT-GT toolchain that tackles the multifaceted challenges associated with ensuring both safety and security. This paper provides a comprehensive description of the toolchain’s architecture and functionalities, including the Attack-Fault Trees generation and model combination approaches. We emphasize the toolchain’s ability to integrate seamlessly with existing systems, allowing for enhanced safety and security analyses without requiring extensive modifications and domain knowledge. Our proposed approach can address evolving security threats, including both known vulnerabilities and emerging attack vectors that could compromise the system. As a use case for the toolchain, we integrate it into the feedback loop of self-adaptive systems. Finally, to validate the practical applicability of the toolchain, we conducted an extensive user study involving domain experts, whose insights and feedback underscore the toolchain’s relevance and usability in real-world scenarios. Our findings demonstrate the toolchain’s effectiveness in real-world applications while highlighting areas for future improvements. The toolchain and associated resources are available in an open-source repository to promote reproducibility and encourage further research in this field.

Cyber–physical systems (CPS) are increasingly self-adaptive, i.e. they have the ability to introspect and change their behavior. This self-adaptation process must be considered when modeling the safety and security aspects of the system. This study collects and compares security attacks and safety hazards on self-adaptive systems (SAS) described in the literature. In addition, mitigation and treatment strategies, as well as the modeling and analysis approaches, are investigated. We conducted a systematic literature review on 21 selected papers. The selection process included a database search on four scientific databases using a common search string (1430 papers), forward and backward snowballing (1402 papers), and filtering the results based on predefined inclusion and exclusion criteria. The coding scheme to analyze the content of the papers was obtained through research questions, existing domain-specific taxonomies, and open coding. Safety and security are not jointly modeled in the context of self-adaptive systems. The adaptation process is often not considered in the attack and hazard analysis due to naïve assumptions and modeling. The proposed approaches are mostly verified and validated through simulation often using simple use cases and scenarios. A thorough and joint modeling approach for safety and security in self-adaptive systems is still an open challenge that needs to be addressed. Further work is needed to address the gap between safety and security modeling in self-adaptive systems. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Given the ongoing "arms race" in cybersecurity, the shortage of skilled professionals in this field is one of the strongest in computer science. The currently unmet staffing demand in cybersecurity is estimated at over 3 million jobs worldwide. Furthermore, the qualifications of the existing workforce are largely believed to be insufficient. We attempt to gain deeper insights into the nature of the current skill gap in cybersecurity. To this end, we correlate data from job ads and academic curricula using two kinds of skill characterizations: manual definitions from established skill frameworks as well as "skill topics" automatically derived by text mining tools. Our analysis shows a strong agreement between these two analysis techniques and reveals a substantial undersupply in several crucial skill categories, e.g., software and application security, security management, requirements engineering, compliance and certification. Based on the results of our analysis, we provide recommendations for future curricula development in cybersecurity so as to decrease the identified skill gaps.

Self-adaptive systems offer several attack surfaces due to the communication via different channels and the different sensors required to observe the environment. Often, attacks cause safety to be compromised as well, making it necessary to consider these two aspects together. Furthermore, the approaches currently used for safety and security analysis do not sufficient take into account the intermediate steps of an adaptation. Current work in this area ignores the fact that a self-adaptive system also reveals possible vulnerabilities (even if only temporarily) during the adaptation. To address this issue, we propose a modeling approach that takes into account the different relevant aspects of a system, its adaptation process, as well as safety hazards and security attacks. We present several models that describe different aspects of a self-adaptive system and we outline our idea of how these models can then be combined into an Attack-Fault Tree. This allows modeling aspects of the system on different levels of abstraction and co-evolve the models using transformations according to the adaptation of the system. Finally, analyses can then be performed as usual on the resulting Attack-Fault Tree.

Over the past few decades, the automotive industry was mostly focused on testing the safety aspects of a vehicle. However, this was not the case with security testing as it only began to be addressed recently. As a result, multiple approaches applying various security testing techniques on different software-based vehicle IT components emerged. With that said, the research and practice lack an overview about these techniques. In this paper, we conduct a systematic mapping study. This involved the investigation on the following five dimensions: (1) security testing techniques, (2) AUTOSAR layers, (3) functional interfaces of AUTOSAR, (4) vehicle lifecycle phases and (5) attacks. In total, 39 papers presenting approaches for security testing in automotive engineering were systematically selected and classified. The results identify multiple security testing techniques focusing on early phases of vehicle life cycle through the application and services layer of the AUTOSAR architecture. Finally, there is a need for security regression testing approaches, as well as combined security and safety testing approaches.

Security testing can broadly be described as (1) the testing of security requirements that concerns confidentiality, integrity, availability, authentication, authorization, nonrepudiation and (2) the testing of the software to validate how much it can withstand an attack. Agile testing involves immediately integrating changes into the main system, continuously testing all changes and updating test cases to be able to run a regression test at any time to verify that changes have not broken existing functionality. Software companies have a challenge to systematically apply security testing in their processes nowadays. There is a lack of guidelines in practice as well as empirical studies in real-world projects on agile security testing; industry in general needs a more systematic approach to security. The findings of this research are not surprising, but at the same time are alarming. The lack of knowledge on security by agile teams in general, the large dependency on incidental pen-testers, and the ignorance in static testing for security are indicators that security testing is highly under addressed and that more efforts should be addressed to security testing in agile teams.