Cyber-Physical and Human-Machine Systems in Systems in Digital Transformation: Challenges of Security and Control

By Prof. Anna Bakurova & Elina Tereschenko*

Under current conditions of digital transformation, the principles of ubiquitous computing, ambient intelligence, and Industry 4.0 are being realized through the integration of advanced computational technologies. These innovations are directed toward increasing the efficiency, adaptability, and intelligence of managerial and operational systems across industrial domains, communication infrastructures, and transportation networks such as rail, road, and aviation.

This transformation entails a profound shift in the architecture and functioning of critical infrastructures. Connectivity, control, and decision-making processes are increasingly defined by real-time data flows, algorithmic governance, and cyber-physical integration. Rather than a mere technological upgrade, digital transformation constitutes a systemic reconfiguration of how value is generated, distributed, and sustained within a tightly interconnected, automated, and information-driven environment.

The concept of digital transformation involves the development of highly automated and intelligently managed environments through the integration of information and communication technologies (ICT), cyber-physical systems (CPS), the Internet of Things (IoT), big data processing technologies, cloud computing services and artificial intelligence (AI) algorithms. The main idea of this paradigm is the close interconnection between digital and physical components, which is provided by embedded sensors, actuators, computing devices and network infrastructure operating in real time..

In the context of digital transformations, human-machine systems (HMS) retain their relevance and serve as a key component in ensuring flexibility, safety, and decision-making support in complex and uncertain environments. Despite the prevailing trend toward maximal automation, the complete removal of humans from critical production and management processes is not only technically impractical but also potentially hazardous, due to the need for cognitive analysis, ethical reasoning, and adaptive responses.

However, the implementation of cyber-physical systems (CPS) and human-machine systems (HMS) is accompanied by a significant increase in cybersecurity threats. The growing interdependence of digital components, the expansion of networked interactions, and the integration of a large number of devices with open interfaces lead to an increasing number of potential attack vectors. Each component becomes a potential point of vulnerability that can be exploited by malicious actors to gain unauthorized access, compromise data integrity, or disrupt critical system elements.

In addition to technical vulnerabilities, the human factor remains one of the weakest links in the cybersecurity infrastructure. A low level of cybersecurity awareness among personnel, disregard for established security policies, unauthorized actions, operator errors, and social engineering techniques can all contribute to the success of an attack, even in the presence of advanced technical protection measures. As a result, there is a growing need for the formalization of access control policies, the implementation of systematic monitoring, and the adoption of adaptive risk management strategies.

Thus, the effective operation of information systems requires a comprehensive approach to cybersecurity—one that takes into account both the technical aspects of system architecture and the human factors associated with interaction and behavior.

 

*Prof. Anna BAKUROVA is a Full doctor, professor of System Analysis and Computational Mathematics at Zaporizhzhia Polytechnic National University.  She holds a Ph.D. in physics and mathematics, 1994.  Member of the public organization “Systems Research”. Her research interests include mathematical methods for Decision Support Systems and mathematical modeling of socio-economic systems.

 

Elina TERESCHENKO, Ph.D. in Mathematical Modeling and Computational Methods, Docent, and specialist in systems analysis, currently serving as Head of the Department of System Analysis and Computational Mathematics at Zaporizhzhia Polytechnic National University, with research focused on mathematical methods for decision support systems, particularly in discrete and multi-criteria optimization.

European Cyber Security Community Initiative (ECSCI)

The European Cyber Security Community Initiative (ECSCI) brings together EU-funded cybersecurity research and innovation projects to foster cross-sector collaboration and knowledge exchange. Its aim is to align technical and policy efforts across key areas such as AI, IoT, 5G, and cloud security. ECSCI organizes joint dissemination activities, public workshops, and strategic dialogue to amplify the impact of individual projects and build a more integrated European cybersecurity landscape.

Supported by the European Commission, ECSCI contributes to shaping a shared vision for cybersecurity in Europe by reinforcing connections between research, industry, and public stakeholders.

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European Cluster for Cybersecurity Certification

The European Cluster for Cybersecurity Certification is a collaborative initiative aimed at supporting the development and adoption of a unified cybersecurity certification framework across the European Union. Bringing together key stakeholders from industry, research, and national authorities, the cluster facilitates coordination, knowledge exchange, and alignment with the EU Cybersecurity Act.

Its mission is to contribute to a harmonized approach to certification that fosters trust, transparency, and cross-border acceptance of cybersecurity solutions. The cluster also works to build a strong stakeholder community that can inform and support the work of the European Union Agency for Cybersecurity (ENISA) and the future European cybersecurity certification schemes.

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CertifAI

CertifAI is an EU-funded project aimed at enabling organizations to achieve and maintain compliance with key cybersecurity standards and regulations, such as IEC 62443 and the EU Cyber Resilience Act (CRA), across the entire product development lifecycle. Rather than treating compliance as a one-time activity or post-development task, CertifAI integrates compliance checks and evidence collection as continuous, embedded practices within daily development and operational workflows.

The CertifAI framework provides structured, practical guidance for planning, executing, and monitoring compliance assessments. It supports organizations in conducting gap analyses, building compliance roadmaps, collecting evidence, and preparing for formal certification. The methodology leverages best practices from established cybersecurity frameworks and aligns with Agile and DevSecOps principles, enabling continuous and iterative compliance checks as products evolve.

A central feature of CertifAI is the use of automation and AI-driven tools—such as Retrieval-Augmented Generation (RAG) systems and Explainable AI—to support the interpretation of complex requirements, detect non-conformities, and generate Security Assurance Cases (SAC) with traceable evidence. The approach is organized into five main phases: preparation and planning, evidence collection and mapping, assessment execution, reporting, and ongoing compliance monitoring.

CertifAI’s methodology is designed to be rigorous yet adaptable, offering organizations a repeatable process to proactively identify, address, and document compliance gaps. This supports organizations not only in meeting certification requirements, but also in embedding a culture of security and compliance into daily practice.

Ultimately, CertifAI’s goal is to make compliance and security assurance continuous, transparent, and integrated, helping organizations efficiently prepare for certification while strengthening their overall cybersecurity posture.

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DOSS

The Horizon Europe DOSS – Design and Operation of Secure Supply Chain – project aims to improve the security and reliability of IoT operations by introducing an integrated monitoring and validation framework to IoT Supply Chains.

DOSS elaborates a “Supply Trust Chain” by integrating key stages of the IoT supply chain into a digital communication loop to facilitate security-related information exchange. The technology includes security verification of all hardware and software components of the modelled architecture. A new “Device Security Passport” contains security-relevant information for hardware devices and their components. 3rd party software, open-source applications, as well as in-house developments are tested and assessed. The centrepiece of the proposed solution is a flexibly configurable Digital Cybersecurity Twin, able to simulate diverse IoT architectures. It employs AI for modelling complex attack scenarios, discovering attack surfaces, and elaborating the necessary protective measures. The digital twin provides input for a configurable, automated Architecture Security Validator module which assesses and provides pre-certification for the modelled IoT architecture with respect of relevant, selectable security standards and KPIs. To also ensure adequate coverage for the back end of the supply chain the operation of the architecture is also be protected by secure device onboarding, diverse security and monitoring technologies and a feedback loop to the digital twin and actors of the supply chain, sharing security-relevant information.

The procedures and technology will be validated in three IoT domains: automotive, energy and smart home.

The 12-member strong DOSS consortium comprises all stakeholders of the IoT ecosystem: service operators, OEMs, technology providers, developers, security experts, as well as research and academic partners.

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EMERALD: Evidence Management for Continuous Compliance as a Service in the Cloud

The EMERALD project aims to revolutionize the certification of cloud-based services in Europe by addressing key challenges such as market fragmentation, lack of cloud-specific certifications, and the increasing complexity introduced by AI technologies. At the heart of EMERALD lies the concept of Compliance-as-a-Service (CaaS) — an agile and scalable approach aimed at enabling continuous certification processes in alignment with harmonized European cybersecurity schemes, such as the EU Cybersecurity Certification Scheme for Cloud Services (EUCS).

By focusing on evidence management and leveraging results from the H2020 MEDINA project, EMERALD will build on existing technological readiness (starting at TRL 5) and push forward to TRL 7. The project’s core innovation is the development of tools that enable lean re-certification, helping service providers, customers, and auditors to maintain compliance across dynamic and heterogeneous environments —including Cloud, Edge, and IoT infrastructures.

EMERALD directly addresses the critical gap in achieving the ‘high’ assurance level of EUCS by offering a technical pathway based on automation, traceability, and interoperability. This is especially relevant in light of the emerging need for continuous and AI-integrated certification processes, as AI becomes increasingly embedded in cloud services.

The project also fosters strategic alignment with European initiatives on digital sovereignty, supporting transparency and trust in digital services. By doing so, EMERALD promotes the adoption of secure cloud services across both large enterprises and SMEs, ensuring that security certification becomes a practical enabler rather than a barrier.

Ultimately, EMERALD’s vision is to provide a robust, flexible, and forward-looking certification ecosystem, paving the way for more resilient, trustworthy, and user-centric digital infrastructures in Europe.

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SEC4AI4SEC

Sec4AI4Sec is a project funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101120393.

This project aims to create a range of cutting-edge technologies, open-source tools, and new methodologies for designing and certifying secure AI-enhanced systems and AI-enhanced systems for security. Additionally, it will provide reference benchmarks that can be utilized to standardize the evaluation of research outcomes within the secure software research community.

The project is divided into two main phases, each with its own name.

·       AI4Sec – stands for using artificial intelligence in security. Democratize security expertise with an AI-enhanced system that reduces development costs and improves software quality. This part of the project improves via AIs the secure coding and testing.

·       Sec4AI –  involves AI-enhanced systems. These systems also have risks that make them vulnerable to new security threats unique to AI-based software, especially when fairness and explainability are essential.

The project considers the economic and technological impacts of combining AI and security.

The economic phase of the project focuses on leveraging AI to drive growth, productivity, and competitiveness across industries. It includes developing new business models, identifying new market opportunities, and driving innovation across various sectors. 

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