Procurement is a critical step in the setup of systems, as reverting decisions made at this point is typically time-consuming and costly. Especially Artificial Intelligence (AI) based systems face many challenges, starting with unclear and unknown side parameters at design time of the systems, changing ecosystems and regulations, as well as problems of overselling capabilities of systems by vendors. Furthermore, the AI Act puts forth a great deal of additional requirements for operators of critical AI systems, like risk management and transparency measures, thus making procurement even more complex. In addition, the number of providers of AI systems is drastically increasing. In this paper we provide guidelines for the procurement of AI based systems that support the decision maker in identifying the key elements for the procurement of secure AI systems, depending on the respective technical and regulatory environment. Furthermore, we provide additional resources for utilizing these guidelines in practical procurement.

Over the years, mobile applications have brought about transformative changes in user interactions with digital services. Many of these apps however, are free and offer convenience at the cost of exchanging personal data. This convenience, however, comes with inherent risks to user privacy and security. This paper introduces a comprehensive methodology that evaluates the risks associated with sharing sensitive data through mobile applications. Building upon the Hierarchical Weighted Risk Scoring Model (HWRSM), this paper proposes an evaluation methodology for HWRSM, keeping in mind the implications of such risk scoring on real-world security scenarios. The methodology employs innovative risk scoring, considering various factors to assess potential security vulnerabilities related to sensitive terms. Practical assessments involving diverse set of Android applications, particularly in data-intensive categories, reveal insights into data privacy practices, vulnerabilities, and alignment with HWRSM scores. By offering insights into testing, validation, real-world findings, and model effectiveness, the paper aims to provide practical considerations to mobile application security discussions, facilitating informed approaches to address security and privacy concerns.

The use of artificial intelligence (AI) in cyber security [1] has proven to be very effective as it helps security professionals better understand, examine, and evaluate possible risks and mitigate them. It also provides guidelines to implement solutions to protect assets and safeguard the technology used. As cyber threats continue to evolve in complexity and scope, and as international standards continuously get updated, the need to generate new policies or update existing ones efficiently and easily has increased [1] [2].The use of (AI) in developing cybersecurity policies and procedures can be key in assuring the correctness and effectiveness of these policies as this is one of the needs for both private organizations and governmental agencies. This study sheds light on the power of AI-driven mechanisms in enhancing digital defense procedures by providing a deep implementation of how AI can aid in generating policies quickly and to the needed level.

Artificial intelligence (AI) has been successfully used in cyber security for enhancing comprehending, investigating, and evaluating cyber threats. It can effectively anticipate cyber risks in a more efficient way. AI also helps in putting in place strategies to safeguard assets and data. Due to their complexity and constant development, it has been difficult to comprehend cybersecurity controls and adopt the corresponding cyber training and security policies and plans.Given that both cyber academics and cyber practitioners need to have a deep comprehension of cybersecurity rules, artificial intelligence (AI) in cybersecurity can be a crucial tool in both education and awareness. By offering an in-depth demonstration of how AI may help in cybersecurity education and awareness and in creating policies fast and to the needed level, this study focuses on the efficiency of AI-driven mechanisms in strengthening the entire cyber security education life cycle.

Artificial Intelligence (AI) holds great potential for enhancing Risk Management (RM) through automated data integration and analysis. While the positive impact of AI in RM is acknowledged, concerns are rising about unintended consequences. This study explores factors like opacity, technology and security risks, revealing potential operational inefficiencies and inaccurate risk assessments. Through archival research and stakeholder interviews, including chief risk officers and credit managers, findings highlight the risks stemming from the absence of AI regulations, operational opacity, and information overload. These risks encompass cybersecurity threats, data manipulation uncertainties, monitoring challenges, and biases in algorithms. The study emphasizes the need for a responsible AI framework to address these emerging risks and enhance the effectiveness of RM processes. By advocating for such a framework, the authors provide practical insights for risk managers and identify avenues for future research in this evolving field.

Artificial intelligence (AI) has emerged as one of the most formative technologies of the century and further gains importance to solve the big societal challenges (e.g. achievement of the sustainable development goals) or as a means to stay competitive in today’s global markets. The role as a key enabler in many areas of our daily life leads to a growing dependence, which has to be managed accordingly to mitigate negative economic, societal or privacy impacts. Therefore, the European Union is working on an AI Act, which defines concrete governance, risk and compliance (GRC) requirements. One of the key demands of this regulation is the operation of a risk management system for High-Risk AI systems. In this paper, we therefore present a detailed analysis of relevant literature in this domain and introduce our proposed approach for an AI Risk Management System (AIRMan).

We propose a new security risk assessment approach for Machine Learning-based AI systems (ML systems). The assessment of security risks of ML systems requires expertise in ML security. So, ML system developers, who may not know much about ML security, cannot assess the security risks of their systems. By using our approach, a ML system developers can easily assess the security risks of the ML system. In performing the assessment, the ML system developer only has to answer the yes/no questions about the specification of the ML system. In our trial, we confirmed that our approach works correctly. CCS CONCEPTS • Security and privacy; • Computing methodologies → Artificial intelligence; Machine learning;

The effective use of artificial intelligence (AI) to enhance cyber security has been demonstrated in various areas, including cyber threat assessments, cyber security awareness, and compliance. AI also provides mechanisms to write cybersecurity training, plans, policies, and procedures. However, when it comes to cyber security risk assessment and cyber insurance, it is very complicated to manage and measure. Cybersecurity professionals need to have a thorough understanding of cybersecurity risk factors and assessment techniques. For this reason, artificial intelligence (AI) can be an effective tool for producing a more thorough and comprehensive analysis. This study focuses on the effectiveness of AI-driven mechanisms in enhancing the complete cyber security insurance life cycle by examining and implementing a demonstration of how AI can aid in cybersecurity resilience.

We propose a conceptual framework, named "AI Security Continuum," consisting of dimensions to deal with challenges of the breadth of the AI security risk sustainably and systematically under the emerging context of the computing continuum as well as continuous engineering. The dimensions identified are the continuum in the AI computing environment, the continuum in technical activities for AI, the continuum in layers in the overall architecture, including AI, the level of AI automation, and the level of AI security measures. We also prospect an engineering foundation that can efficiently and effectively raise each dimension.

The authors clarified in 2020 that the relationship between AI and security can be classified into four categories: (a) attacks using AI, (b) attacks by AI itself, (c) attacks to AI, and (d) security measures using AI, and summarized research trends for each. Subsequently, ChatGPT became available in November 2022, and the various potential applications of ChatGPT and other generative AIs and the associated risks have attracted attention. In this study, we examined how the emergence of generative AI affects the relationship between AI and security. The results show that (a) the need for the four perspectives of AI and security remains unchanged in the era of generative AI, (b) The generalization of AI targets and automatic program generation with the birth of generative AI will greatly increase the risk of attacks by the AI itself, (c) The birth of generative AI will make it possible to generate easy-to-understand answers to various questions in natural language, which may lead to the spread of fake news and phishing e-mails that can easily fool many people and an increase in AI-based attacks. In addition, it became clear that (1) attacks using AI and (2) responses to attacks by AI itself are highly important. Among these, the analysis of attacks by AI itself, using an attack tree, revealed that the following measures are needed: (a) establishment of penalties for developing inappropriate programs, (b) introduction of a reporting system for signs of attacks by AI, (c) measures to prevent AI revolt by incorporating Asimov s three principles of robotics, and (d) establishment of a mechanism to prevent AI from attacking humans even when it becomes confused.

The traditional port smart gate ground scale line pressure detection system employs a centralized data training method that carries the risk of privacy leakage. Federated Learning offers an effective solution to this issue by enabling each port gate to locally train data, sharing only model parameters, without the need to transmit raw data to a central server. This is particularly crucial for ground scale line pressure detection systems dealing with sensitive data. However, researchers have identified potential risks of backdoor attacks when applying Federated Learning. Currently, most existing backdoor attacks are directed towards image classification and centralized object detection. However, backdoor attacks for Federated Learning-based object detection tasks have not been explored. In this paper, we reveal that these threats may also manifest in this task. To analyze the impact of backdoor attacks on this task, we designed three backdoor attack triggers and proposed three trigger attack operations. To assess backdoor attacks on this task, we developed corresponding metrics and conducted experiments on local datasets from three port gates. The experimental results indicate that Federated Learning-based object detection tasks are susceptible to backdoor threats.

The Internet of Things (IoT) heralds a innovative generation in communication via enabling regular gadgets to supply, receive, and percentage records easily. IoT applications, which prioritise venture automation, aim to present inanimate items autonomy; they promise increased consolation, productivity, and automation. However, strong safety, privateness, authentication, and recuperation methods are required to understand this goal. In order to assemble give up-to-quit secure IoT environments, this newsletter meticulously evaluations the security troubles and risks inherent to IoT applications. It emphasises the vital necessity for architectural changes.The paper starts by conducting an examination of security worries before exploring emerging and advanced technologies aimed at nurturing a sense of trust, in Internet of Things (IoT) applications. The primary focus of the discussion revolves around how these technologies aid in overcoming security challenges and fostering an ecosystem for IoT.

The Internet of Things (IoT) refers to the growing network of connected physical objects embedded with sensors, software and connectivity. While IoT has potential benefits, it also introduces new cyber security risks. This paper provides an overview of IoT security issues, vulnerabilities, threats, and mitigation strategies. The key vulnerabilities arising from IoT s scale, ubiquity and connectivity include inadequate authentication, lack of encryption, poor software security, and privacy concerns. Common attacks against IoT devices and networks include denial of service, ransom-ware, man-in-the-middle, and spoofing. An analysis of recent literature highlights emerging attack trends like swarm-based DDoS, IoT botnets, and automated large-scale exploits. Recommended techniques to secure IoT include building security into architecture and design, access control, cryptography, regular patching and upgrades, activity monitoring, incident response plans, and end-user education. Future technologies like blockchain, AI-enabled defense, and post-quantum cryptography can help strengthen IoT security. Additional focus areas include shared threat intelligence, security testing, certification programs, international standards and collaboration between industry, government and academia. A robust multilayered defense combining preventive and detective controls is required to combat rising IoT threats. This paper provides a comprehensive overview of the IoT security landscape and identifies areas for continued research and development.

As a recent breakthrough in generative artificial intelligence, ChatGPT is capable of creating new data, images, audio, or text content based on user context. In the field of cybersecurity, it provides generative automated AI services such as network detection, malware protection, and privacy compliance monitoring. However, it also faces significant security risks during its design, training, and operation phases, including privacy breaches, content abuse, prompt word attacks, model stealing attacks, abnormal structure attacks, data poisoning attacks, model hijacking attacks, and sponge attacks. This paper starts from the risks and events that ChatGPT has recently faced, proposes a framework for analyzing cybersecurity in cyberspace, and envisions adversarial models and systems. It puts forward a new evolutionary relationship between attackers and defenders using ChatGPT to enhance their own capabilities in a changing environment and predicts the future development of ChatGPT from a security perspective.

The adoption of IoT in a multitude of critical infrastructures revolutionizes several sectors, ranging from smart healthcare systems to financial organizations and thermal and nuclear power plants. Yet, the progressive growth of IoT devices in critical infrastructure without considering security risks can damage the user’s privacy, confidentiality, and integrity of both individuals and organizations. To overcome the aforementioned security threats, we proposed an AI and onion routing-based secure architecture for IoT-enabled critical infrastructure. Here, we first employ AI classifiers that classify the attack and non-attack IoT data, where attack data is discarded from further communication. In addition, the AI classifiers are secure from data poisoning attacks by incorporating an isolation forest algorithm that efficiently detects the poisoned data and eradicates it from the dataset’s feature space. Only non-attack data is forwarded to the onion routing network, which offers triple encryption to encrypt IoT data. As the onion routing only processes non-attack data, it is less computationally expensive than other baseline works. Moreover, each onion router is associated with blockchain nodes that store the verifying tokens of IoT data. The proposed architecture is evaluated using performance parameters, such as accuracy, precision, recall, training time, and compromisation rate. In this proposed work, SVM outperforms by achieving 97.7\% accuracy.

This survey paper provides an overview of the current state of AI attacks and risks for AI security and privacy as artificial intelligence becomes more prevalent in various applications and services. The risks associated with AI attacks and security breaches are becoming increasingly apparent and cause many financial and social losses. This paper will categorize the different types of attacks on AI models, including adversarial attacks, model inversion attacks, poisoning attacks, data poisoning attacks, data extraction attacks, and membership inference attacks. The paper also emphasizes the importance of developing secure and robust AI models to ensure the privacy and security of sensitive data. Through a systematic literature review, this survey paper comprehensively analyzes the current state of AI attacks and risks for AI security and privacy and detection techniques.

Artificial intelligence (AI) was engendered by the rapid development of high and new technologies, which altered the environment of business financial audits and caused problems in recent years. As the pioneers of enterprise financial monitoring, auditors must actively and proactively adapt to the new audit environment in the age of AI. However, the performances of the auditors during the adaptation process are not so favorable. In this paper, methods such as data analysis and field research are used to conduct investigations and surveys. In the process of applying AI to the financial auditing of a business, a number of issues are discovered, such as auditors' underappreciation, information security risks, and liability risk uncertainty. On the basis of the problems, related suggestions for improvement are provided, including the cultivation of compound talents, the emphasis on the value of auditors, and the development of a mechanism for accepting responsibility.