Context: Placing Artificial Intelligence (AI) in control of cybersecurity compliance and auditing shifts its role from decision-support to direct execution of regulatory operational processes, where AI outputs may constitute compliance artefacts and audit evidence. This raises the problem of Meta-Compliance , in which not only the organisation but also the AI system must satisfy enforceable requirements. Yet existing frameworks provide no operational criteria for recognising AI as authoritative in such roles. Trustworthy AI principles define high-level Second-Layer requirements but remain non-binding, whereas First-Layer organisational requirements impose explicit justificatory and evidentiary duties. Objectives: This study investigates the minimal normative conditions under which AI systems can be recognised as authoritative in compliance and auditing, capable of producing evidence valid for assurance. Methods: Doctrinal analysis is conducted on binding “shall/must” provisions across PCI DSS, DORA, UK GDPR, NIS2, ISO/IEC 27001, and NIST SP 800-53. Provisions are normalised through the compliance–audit chain ( requirement → control → rule → evidence ) and mapped against Second-Layer AI governance requirements. The result is the Compliance–Audit Authority Benchmark (CAAB) , comprising six criteria: Traceability, Explainability, Evidence Integrity, Adaptability, Action Governance, and Reasoning. Results: Applying CAAB across AI model families and architectures shows that symbolic and knowledge-representation methods satisfy most criteria intrinsically, whilst neural, deep, and generative models do not unless supported by external governance mechanisms. This exposes a structural gap between First-Layer organisational requirements and Second-Layer AI requirements, clarifying that authority rests on evidentiary guarantees rather than statistical accuracy. Conclusion: The study formalises Meta-Compliance as the recursive structure in which both organisations and AI systems become subjects of assurance. CAAB defines the minimum conditions for recognising AI as authoritative, whilst the proposed Verifiable Reasoning Architecture (VRA) may offer a pathway toward AI systems anchored in secured evidence, reproducible inference, and symbolic governance, establishing audit-ready authority in high-risk contexts.

In this paper, we introduce a new trusted opportunistic routing protocol called Trusted Context-aware Opportunistic Routing (TCOR) which produces a secured routing for VANET and it is incorporated with a recommendation mechanism (TCOR-Rec). OMNET++ is used to implement and simulate TCOR, which has proved its efficiency in comparison with other routing protocols (COR opportunistic protocol, AODV and trusted AODV). To simulate an adequate VANET environment and evaluate the protocols under realistic conditions, different metrics and network parameters such as the network traffic pattern, mobility pattern, and the fading propagation model have been used. The results have shown that TCOR and TCOR-Rec outperform traditional routing protocols by approximately 9% and 15%, respectively, in terms of packet delivery when the attacker nodes are involved in the network.

The growth of connected vehicles and their associated services has endowed them with the remarkable ability to rapidly generate vast volumes of data. This proliferation has led to an increasing demand for effective data governance solutions. This paper delves into the exploration of currently available in-vehicle data, meticulously assessing the aspects of data velocity and heterogeneity. By scrutinising these factors, the paper aims to pinpoint and address critical gaps in how to deal with in-vehicle data, ultimately striving to create a seamless platform for managing and harnessing in-vehicle data. This project explores approaches for various connected vehicle communications, including V2V, V2I, and V2X, to de?ne data feeds in the connected vehicle data landscape. The results of the study could in uence the design of in-vehicle data governance by providing information on a stronger integrated framework, helping data owners and users make informed decisions about managing their data assets.

As connected vehicles continue to become more advanced and widespread, it is vital to ensure that vehicle to everything (V2X) communications are secure. Connected car mobile applications have become a key feature in the vehicular ecosystem and represent a new attack vector that can be used to compromise vehicles. This paper undertakes a threat analysis and risk assessment (TARA) of connected car mobile applications using known vulnerabilities. The found vulnerabilities are analysed to highlight common weaknesses. This paper thus outlines how a TARA can be adapted in the context of connected car mobile applications with reference to established security models.

The healthcare industry deals with highly sensitive data which must be managed in a secure way. Electronic Health Records (EHRs) hold various kinds of personal and sensitive data which contain names, addresses, social security numbers, insurance numbers, and medical history. Such personal data is valuable to the patients, healthcare service providers, medical insurance companies, and research institutions. However, the public release of this highly sensitive personal data poses serious privacy and security threats to patients and healthcare service providers. Hence, we foresee the requirement of new technologies to address the privacy and security challenges for personal data in healthcare applications. Blockchain is one of the promising solutions, aimed to provide transparency, security, and privacy using consensus-driven decentralised data management on top of peer-to-peer distributed computing systems. Therefore, to solve the mentioned problems in healthcare applications, in this paper, we investigate the use of private blockchain technologies to assess their feasibility for healthcare applications. We create testing scenarios using HyperLedger Fabric to explore different criteria and use-cases for healthcare applications. Additionally, we thoroughly evaluate the representative test case scenarios to assess the blockchain-enabled security criteria in terms of data confidentiality, privacy and access control. The experimental evaluation reveals the promising benefits of private blockchain technologies in terms of security, regulation compliance, compatibility, flexibility, and scalability.

Vehicular ad hoc network (VANET), a novel technology, holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as man-in-the-middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate, and trusted content within the network. In this article, we propose a novel trust model, namely, MiTM attack resistance trust model in connected vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multidimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data are then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the benchmarked trust model. The simulation results show that for a network containing 35% of MiTM attackers, MARINE outperforms the state-of-the-art trust model by 15%, 18%, and 17% improvements in precision, recall, and F-score, respectively.

Vehicular Ad-hoc NETworks (VANET) have revolutionised the intelligent transportation systems. Indeed, they enable vehicles to communicate with each other and with the infrastructure, and they facilitate several vital applications in real-time. Several trust models have been proposed to ensure a secured VANET and the authenticity, integrity and reliability of information exchanged in the network. The concept of trust models is to introduce and implement trust explicitly in the vehicular nodes. In this paper, we propose a lightweight evaluation methodology to evaluate trust models, specifically designed for VANET. Our study focused on the three categories of VANET trust models (TMs): Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Combined Trust Models (CTM). Our simulations evaluate the efficiency of the trust models and compare them against several trust related parameters: false positive rate, precision and accuracy level in detecting malicious nodes. Since the scope of this research work is to evaluate the performance of TMs under adversary conditions, we have considered on-off attacks which can act as man-in-the-middle to drop and delay transmitted packets.

The emerging technologies of Smart Camera Sensor Networks (SCSN) are being driven by the social need for security assurance and analytical information. SCSN are deployed for protection and for surveillance tracking of potential criminals. A smart camera sensor does not just capture visual and audio information but covers the whole electromagnetic spectrum. It constitutes of intelligent onboard processor, autonomous communication interfaces, memory and has the ability to execute algorithms. The rapid deployment of smart camera sensors with ubiquitous imaging access causes security and privacy issues for the captured data and its metadata, as well as the need for trust and cooperation between the smart camera sensors. The intelligence growth in this technology requires adequate information security with capable privacy and trust protocols to prevent malicious content attacks. This paper presents, first, a clear definition of SCSN. It addresses current methodologies with perspectives in privacy and trust protection, and proposes a multi-layer security approach. The proposed approach highlights the need for a public key infrastructure layer in association with a Reputation-Based Cooperation mechanism.