Multi-Layered NGFW Protection Shield for AI Infrastructure

arXiv, 2025

The rapid digitalization of communication systems has elevated Interactive Voice Response (IVR) technologies to become critical interfaces for customer engagement. With Artificial Intelligence (AI) now driving these platforms, ensuring secure, compliant, and ethically designed development practices is more imperative than ever. AI-powered IVRs leverage Natural Language Processing (NLP) and Machine Learning (ML) to personalize interactions, automate service delivery, and optimize user experiences. However, these innovations expose systems to heightened risks, including data privacy breaches, AI decision opacity, and model security vulnerabilities. This paper analyzes the evolution of IVRs from static code-based designs to adaptive AI-driven systems, presenting a cybersecurity-centric perspective. We propose a practical governance framework that embeds agile security principles, compliance with global data legislation, and user-centric ethics. Emphasizing privacy-by-design, adaptive risk modeling, and transparency, the paper argues that ethical AI integration is not a feature but a strategic imperative. Through this multidimensional lens, we highlight how modern IVRs can transition from communication tools to intelligent, secure, and accountable digital frontlinesresilient against emerging threats and aligned with societal expectations.

Business Informatics

This article is devoted to aspects associated with the up-coming need for mass implementation of neural networks in the modern society. On the one hand, the latter will fully expand the capabilities of state institutions and society delegated to perform numerous tasks with higher efficiency. However, a significant threat to democratic institutions obliges society to set out the concept of reliable artificial intelligence (AI). The authors explore a new concept of a trusted AI necessary for the scientific and international community to counter improper future digital penetration. Explaining to what extent digital transformation is mandatory, the authors emphasize the numerous dangers associated with the applications of artificial intelligence. The purpose of the article is to study the potential hazards of neural networks’ abuse by the authorities and the resistance to them with reliance on the trusted AI. Studying various aspects of digital transformation and the use of artificial i...

IGI Global, 2024

This chapter discusses the technological and socio-political challenges of integrating artificial intelligence (AI) into digital ecosystems. Understanding the power structures, ideologies, and discourses that shape cybersecurity AI perception and response is crucial. The Twitter AI-powered algorithm, the Autonomous Vehicle Accident, and the Microsoft Tay Chatbot controversy demonstrate the risks and ethical issues of AI in cybersecurity. Future possibilities include AI-enabled network intrusions, social engineering attacks, and deepfake technology for cyber espionage. The chapter discusses GDPR and CISA and emphasizes the need for AI-specific norms and internationally recognized standards to promote ethical, transparent, and responsible AI application in cybersecurity. The idea improves frameworks by integrating governance controls and creating customized risk management requirements for cybersecurity AI. Openness, accountability, and ethics are stressed to improve COBIT implementation.

Frontiers in Artificial Intelligence

Privacy remains one of the most recurrent concerns that people have about AI technologies. The meaning of the concept of “privacy” has proven to be fairly elusive. Accordingly, the concerns people have about privacy are often vague and ill-formed, which makes it correspondingly difficult to address these concerns, and to explain the ways in which AI technologies do or do not pose threats to people's interests. In this article, we draw attention to some important distinctions that are frequently overlooked, and spell out their implications for concerns about the threats that AI-related technology poses for privacy. We argue that, when people express concerns about privacy in relation to AI technologies, they are usually referring to security interests rather than interests in privacy per se. Nevertheless, we argue that focusing primarily on security interests misses the importance that interests in privacy per se have through their contribution to autonomy and the development of ...

Securing AI Algorithms Against Cyber Attacks, 2023

As artificial intelligence (AI) becomes increasingly integrated into our daily lives, the risk of cyber attacks targeting AI algorithms is growing. AI algorithms are vulnerable to a range of attacks, including data poisoning, model inversion, and adversarial examples, which can undermine the integrity of the algorithms and compromise the security of the systems that rely on them. This paper examines the techniques for securing AI algorithms against cyber attacks, including the use of encryption, anomaly detection, and adversarial training. The paper also discusses the challenges of securing AI in real-world applications, such as autonomous vehicles and medical diagnosis systems. Furthermore, the paper explores the ethical considerations surrounding the use of AI in security, including the potential for bias and the need for transparency and accountability. Ultimately, the paper argues that securing AI algorithms against cyber attacks must be a top priority for researchers and developers in the field of AI, as well as policymakers and regulators. If you are interested in the future of AI and the challenges of securing these powerful technologies, this paper provides valuable insights into a critical area of research and development.

International Journal of Innovative Science and Research Technology (IJISRT), 2024

Cyber threats' increasing magnitude and intricacy require a fundamental change in security operations. Conventional approaches face difficulties in keeping up, which exposes organizations to risks. This paper examines the expanding attack surface: securing AI and machine learning systems in security operations as a remedy. A literature review, informed by the Diffusion of Innovation Theory, investigates how organizations absorb innovations in this study. The results demonstrate notable benefits of AI/ML in security, such as superior identification of threats, improved efficiency through automation, and optimized management of vulnerabilities. Nevertheless, achieving successful execution necessitates meticulous deliberation of obstacles. These tasks encompass guaranteeing data accuracy, preserving the capacity to understand how models work, reducing any potential prejudices in AI/ML models, and resolving security weaknesses in the systems themselves. The paper also discusses ethical considerations and emphasizes the important function of human monitoring. To address these difficulties, the study recommends prioritizing data quality, utilizing explainable AI methods, and developing tactics to mitigate bias. Furthermore, there is a strong emphasis on using a human-in-the-loop strategy to take advantage of humans' expertise and machine-learning capabilities. This study highlights the capacity of artificial intelligence and machine learning to transform security operations completely. By confronting the recognized obstacles, organizations may unleash the genuine potential of these technologies and establish a stronger and more proactive security position in response to constantly changing cyber threats.

2019

Applications of artificial intelligence (AI) for cybersecurity tasks are attracting greater attention from the private and the public sectors. Estimates indicate that the market for AI in cybersecurity will grow from US$1 billion in 2016 to a US$34.8 billion net worth by 2025. The latest national cybersecurity and defense strategies of several governments explicitly mention AI capabilities. At the same time, initiatives to define new standards and certification procedures to elicit users’ trust in AI are emerging on a global scale. However, trust in AI (both machine learning and neural networks) to deliver cybersecurity tasks is a double-edged sword: it can improve substantially cybersecurity practices, but can also facilitate new forms of attacks to the AI applications themselves, which may pose severe security threats. We argue that trust in AI for cybersecurity is unwarranted and that, to reduce security risks, some form of control to ensure the deployment of ‘reliable AI’ for cybersecurity is necessary. To this end, we offer three recommendations focusing on the design, development and deployment of AI for cybersecurity.

IGI Global, 2024

As AI proliferates across sectors, it creates new cybersecurity risks from growing attack surfaces, data flows, and system complexity. This chapter outlines risk management frameworks to harness AI safely despite escalating threats. It establishes why traditional controls now fall short, necessitating updated cyber strategies centered on ethical "Secure AI by Design" governance. First, prominent threats like malware and denial-of-service attacks are analyzed. Technical safeguards such as authentication, encryption, and blockchain applications are suggested alongside auditing, transparency, and proactive risk monitoring to manage threats. Real-world critical infrastructure attack cases reveal current susceptibilities. esilience demands optimization coupled with defense-in-depth approaches across people, processes and system that gives advisory on adapting cybersecurity and a guide to securing AI innovation potential, aligning with the book's focus on OpenAI outlines steps around pipelines, red teams, and internal/external trust via auditing and transparency for cyber risk management

Cogent Social Sciences, 2025

The European Union’s Artificial Intelligence Act (AI Act) establishes a novel regulatory framework for AI systems, with far-reaching implications for data protection compliance. This study critically analyses the regulatory environment created by the Act and its interaction with the General Data Protection Regulation (GDPR), addressing whether it provides clearer compliance pathways or introduces additional burdens for developers. Using comparative legal analysis, the research finds that the AI Act supplements the GDPR through a risk-based approach that subjects high-risk AI systems to specific obligations. Key concerns include algorithmic opacity, bias, and legal uncertainty in profiling and automated decision-making. The analysis shows that the AI Act attempts to mitigate these risks by strengthening requirements on risk assessment, human oversight, and data governance. While the Act broadly aligns with GDPR principles such as transparency, fairness, and accountability, it also introduces new procedural and documentation duties that may increase compliance complexity. Overall, the study concludes that the AI Act constitutes a complementary yet stricter regulatory layer for AI-driven data processing, balancing innovation with fundamental rights protection and requiring joint interpretation with the GDPR to ensure coherent application.

In a digital era marked by rapid technological advancement, artificial intelligence (AI) offers governments an unprecedented tool to enhance cybersecurity and safeguard sensitive information. This article explores the transformative potential of AI in government cybersecurity, focusing on how it can bolster public trust. By analyzing recent findings, including applications in threat detection, automated response, and predictive analytics, this article outlines the benefits, challenges, and necessary steps for the successful implementation of AI in government cybersecurity.