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Journal articles on the topic 'AI security'
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Yogesh Kumar Bhardwaj. "Securing Generative AI: Navigating Data Security Challenges in the AI Era." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 147–55. https://doi.org/10.32996/jcsts.2025.7.4.17.
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This article examines the evolving security landscape for Generative Artificial Intelligence (GenAI) systems as they become increasingly integrated across critical sectors including healthcare, finance, and transportation. The proliferation of these technologies creates both transformative opportunities and significant security challenges that require specialized approaches. It explores key security vulnerabilities unique to GenAI implementations, including data protection vulnerabilities, access control complexities, data anonymization gaps, model integrity risks, monitoring challenges, intellectual property concerns, and regulatory compliance issues. Building upon current research, the article presents a comprehensive security framework encompassing data protection strategies, access control mechanisms, model security approaches, network security architectures, monitoring frameworks, compliance guidelines, incident response methodologies, and zero trust principles. Organizations implementing these strategies demonstrate substantially improved security outcomes, including faster threat detection, reduced breach incidents, and enhanced resilience against emerging attack vectors. It underscores the necessity for purpose-built security approaches that address the unique characteristics of GenAI systems, requiring close collaboration between industry stakeholders, policymakers, and security practitioners to establish robust defensive frameworks while enabling continued innovation.
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Poongodi, R. K. "Zero Trust AI Authentication and Blockchain Powered Secure AI." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 52–56. https://doi.org/10.22214/ijraset.2025.66800.
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The integration of Zero Trust (ZT) security models with Artificial Intelligence (AI) authentication mechanisms, along with the utilization of blockchain technology, offers a novel paradigm for securing digital interactions and data exchanges in increasingly decentralized and complex networks. Zero Trust, a security framework that assumes no implicit trust and enforces strict identity verification, is well-suited for AI-driven authentication systems that require robust, real-time, and adaptive security measures Blockchain technology further enhances this framework by providing a transparent, immutable ledger for logging authentication events, access control decisions, and transactions, ensuring tamper-proof audit trails. Blockchain’s decentralized nature also mitigates single points of failure, improving resilience and privacy. When combined, Zero Trust, AI, and blockchain can deliver an advanced, self-evolving security system that both anticipates and responds to new threats with greater precision and efficiency.
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Chiranjeevi, G., R. Abhishek Reddy, R. Shyam, Swati Sah, and Rejwan Bin Sulaiman. "Docker Based Decentralized Vulnerability Assessment with Port Scanning Powered by Artificial Intelligence." FMDB Transactions on Sustainable Intelligent Networks 1, no. 4 (2024): 220–41. https://doi.org/10.69888/ftsin.2024.000290.
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Decentralized solutions, widely adopted across industries like banking, health- care, and logistics, face persistent security concerns from potential threats. This study introduces a novel decentralized vulnerability assessment using GPT-3, an artificial intelligence (AI) technology. Employing Dockerized containers for disinfecting environments and creating unique connections to the AI API service enhances system responsiveness. AI algorithms, specifically GPT-3, conduct comprehensive network scans to identify security flaws. Findings are securely distributed to network nodes, fortifying the system’s defence. This departure from centralized control and traditional security audits marks a significant advancement in securing decentralized systems. AI-enabled real-time monitoring facilitates swift responses to security issues, reducing breach risks and aiding effective resource management. Encouraging results from controlled system analysis, focusing on GPT-3 vulnerabilities, highlight the integration of Dockerized containers for enhanced system efficiency. This work lays the foundation for further research, emphasizing the potential of decentralized systems for rigorous security assessments.
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Chen, Hsinchun. "AI and Security Informatics." IEEE Intelligent Systems 25, no. 5 (2010): 82–90. http://dx.doi.org/10.1109/mis.2010.116.
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Agrawal, Jatin, Samarjeet Singh Kalra, and Himanshu Gidwani. "AI in cyber security." International Journal of Communication and Information Technology 4, no. 1 (2023): 46–53. http://dx.doi.org/10.33545/2707661x.2023.v4.i1a.59.
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BS, Guru Prasad, Dr Kiran GM, and Dr Dinesha HA. "AI-Driven cyber security: Security intelligence modelling." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 6 (2023): 961–65. http://dx.doi.org/10.54660/.ijmrge.2023.4.6.961-965.
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The process of defending computer networks from cyber attacks or unintended, unauthorized access is known as cyber security. Organizations, businesses, and governments need cyber security solutions because cyber criminals pose a threat to everyone. Artificial intelligence promises to be a great solution for this. Security experts are better able to defend vulnerable networks and data from cyber attackers by combining the strengths of artificial intelligence and cyber security. This paper provides an introduction to the use of artificial intelligence in cyber security. AI-driven cyber security refers to the use of artificial intelligence and machine learning technologies to enhance the protection of computer systems and networks from cyber threats such as hacking, malware, phishing, and other forms of cyberattacks. AI-powered security solutions are designed to automate the process of detecting, analyzing, and responding to security incidents in real-time, thereby improving the efficiency and effectiveness of cyber defense. These solutions can analyze large amounts of data, identify patterns and anomalies, and make decisions faster and more accurately than humans alone, enabling organizations to stay ahead of evolving cyber threats.
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Abudalou, Mohammad Ali. "Security DevOps: Enhancing Application Delivery with Speed and Security." International Journal of Computer Science and Mobile Computing 13, no. 5 (2024): 100–104. http://dx.doi.org/10.47760/ijcsmc.2024.v13i05.009.
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"Protection DevOps: Security DevOps: Enhancing Application Delivery with Speed and Security" is a whole paper that explores the combination of synthetic intelligence (AI) technology into the protection DevOps framework. This integration goals to enhance software transport with the aid of AI-driven automation, predictive analytics, and chance intelligence. Inside the contemporary, rapid-paced virtual panorama, groups face developing pressure to deliver applications quickly while making sure sturdy safety capabilities are in proximity. The traditional method of protection frequently results in delays in deployment and hampers agility. With the aid of incorporating AI capabilities into DevOps protection practices, agencies can achieve stability among pace and safety. This paper examines how AI can optimize several factors of safety DevOps, together with: Automatic threat detection: AI-powered equipment can look at massive quantities of data in real-time to proactively come across and respond to protection threats. This functionality enables figuring out anomalies, predicting functionality dangers, and taking preemptive actions. Practical safety finding out: AI-pushed attempting out tools can carry out complete protection finding out, which includes vulnerability exams, penetration sorting out, and code assessment. Those gear leverage gadgets, studying algorithms to perceive safety gaps and advocate remediation measures. Predictive risk management: AI algorithms can examine historical protection statistics and patterns to expect future protection dangers. This proactive method lets companies put in place location-specific preventive measures and restrict the effects of safety incidents. Non-forestall compliance monitoring: AI-based total compliance system can display and implement regulatory compliance requirements at some point inside the improvement and deployment lifecycle. This guarantees that applications adhere to enterprise standards and regulatory hints. By incorporating AI into DevOps safety, businesses can gather accelerated software shipping without compromising protection. The synergistic aggregate of AI-driven automation, predictive analytics, and threat intelligence empowers DevOps organizations to respond to growing threats while preserving an excessive protection posture. This paper offers insights into the benefits of integrating AI into safety DevOps practices, uncommon use times, implementation strategies, and great practices. It courses agencies looking for to leverage AI technologies to decorate utility shipping with velocity and safety in a dynamic virtual environment.
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Poonia, Ramesh Chandra. "Securing the Sustainable Future : Cryptography and Security in AI & IoT." Journal of Discrete Mathematical Sciences and Cryptography 27, no. 4 (2024): i—vii. http://dx.doi.org/10.47974/jdmsc-27-4-foreword.
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The objective of this special issue titled “Securing the Sustainable Future: Cryptography and Security in AI & IoT” is to discuss recent advancements of cryptography and security in AI & IoT for a secure, sustainable future. The issue focuses on the social and economic impact of sustainable computing, with sub-topics covering the applications of AI & IoT. Additionally, the conference has explored security mechanisms in the context of sustainability.
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Gudimetla, Sandeep Reddy, and Niranjan Reddy Kotha. "AI-POWERED THREAT DETECTION IN CLOUD ENVIRONMENTS." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, no. 1 (2018): 638–42. http://dx.doi.org/10.61841/turcomat.v9i1.14730.
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This study assesses the effectiveness of artificial intelligence (AI) technologies in enhancing threat detection within cloud environments, a critical component given the escalating security challenges in cloud computing. Leveraging various AI methodologies, including machine learning models, deep learning, and anomaly detection techniques, the research aims to improve the accuracy and efficiency of security systems. These AI methods were applied to a series of simulated threat scenarios across diverse cloud platforms to evaluate their capability in real-time threat identification and mitigation. Results demonstrated a significant enhancement in detection rates and a decrease in false positives, indicating that AI can substantially improve the robustness of cloud security systems against sophisticated cyber threats. The study highlights the transformative potential of AI in cloud security, showing not only improvements in threat detection but also in the speed and reliability of responses to security incidents. Furthermore, the findings advocate for the integration of AI technologies into existing cloud security infrastructures to achieve more dynamic and adaptable security solutions. The conclusion points towards the need for ongoing research into advanced AI applications in cloud security, suggesting future directions such as the development of self-learning security systems and the exploration of AI's predictive capabilities in pre-empting security breaches. This research provides a foundation for further exploration and potential real-world application of AI in securing cloud environments against an increasingly complex landscape of cyber threats.
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Sohaib uz Zaman, Erum Parveen, and Syed Hasnain Alam. "AI to Enhance the Transactional Security in Digital Banking." Journal of Management & Social Science 2, no. 2 (2025): 1–22. https://doi.org/10.63075/2apb3z22.
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The integration of artificial intelligence (AI) has enhanced banking operations by providing improved customer service, security, and efficiency. For data privacy and security, Banks must implement robust data security measures to gain customer trust as well as comply with regulatory requirements. Therefore, this study examined whether AI-based security systems can augment transactional safety, secure pay systems, and strengthen customer trust in digital banks. Quantitative research design and stratified random sampling technique is used for data collection. Questionnaire is filled up with digital banking customers and professionals. Descriptive statistic is used to understand the demographics of respondent, Cronbach’s alpha test for reliability e and regression analysis is applied to analyze how AI can enhance the transactional security in digital banking. The findings point out that AI techniques are most influential on transaction security and customer trust, and powered security systems play an auxiliary role in securing payment systems. The digital banking platforms are also significant mediators that make both AI techniques and powered security systems more effective. AI-based security solutions, such solutions provide extensive protection against cyber threats and improve the user experience to bring about easy and hassle-free transactions with the help of different features like AI chatbots, real-time fraud alerts, and automated dispute resolution add up to make the banking environment secure and customer-friendly. Keywords: AI techniques, transactional security, payment system security, digital banking platforms, customer trust and powered security systems.
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Reddy, Haritha Madhava. "Role of AI in Security Compliance." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem32650.
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Abstract—Artificial Intelligence (AI) has emerged as a pivotal tool in enhancing security compliance across various industries. Its ability to analyze vast datasets, detect intricate patterns, and automate complex processes significantly improves risk management and regulatory adherence. AI enables real-time data analysis, promptly identifying potential violations and flagging security threats, thereby strengthening an organization’s overall security framework. However, while AI offers transformative advantages, its integration into existing security systems introduces new challenges, such as data privacy concerns, algorithmic bias, and the need for transparent decision-making. This paper explores the dual role of AI in both enhancing compliance efforts and presenting risks that require careful management. By adopting a balanced approach—leveraging AI’s capabilities while ensuring robust oversight— organizations can optimize compliance processes, address regulatory challenges, and mitigate associated risks. Achieving this balance is critical to securing long-term success in an increasingly regulated and digitized landscape. Keywords—Artificial Intelligence (AI), security compliance, risk management, regulatory compliance, data privacy, algorithmic bias, real-time data analysis, threat detection, automation, cybersecurity, transparency, decision-making, compliance automation, AI integration, ethical AI deployment, organizational security, regulatory frameworks.
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Sengupta, Abhijeet. "Securing the Autonomous Future A Comprehensive Analysis of Security Challenges and Mitigation Strategies for AI Agents." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–2. https://doi.org/10.55041/ijsrem40091.
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The proliferation of Artificial Intelligence (AI) agents, characterized by their autonomy and capacity for independent decision-making, presents both unprecedented opportunities and novel security challenges. This research paper provides a comprehensive analysis of the security landscape surrounding AI agents, examining the unique vulnerabilities stemming from their inherent characteristics and the emerging threat vectors targeting these autonomous systems. We delve into a categorized framework of potential attacks, ranging from data poisoning and adversarial manipulation to physical tampering and exploitation of autonomy. Furthermore, we critically evaluate existing and propose novel mitigation strategies, encompassing secure development practices, robustness training, explainable AI techniques for monitoring, and the crucial role of ethical and regulatory frameworks. This paper contributes to the growing body of knowledge on AI security, offering insights for researchers, developers, and policymakers navigating the complexities of securing the autonomous future. Keywords: Artificial Intelligence, AI Agents, Autonomous Systems, Cybersecurity, Machine Learning Security, Adversarial Attacks, Data Poisoning, Robotics Security, Ethical AI, AI Governance
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Prashant, Kailas Awasare. "Role of Artificial Intelligence in Identity and Access Management." International Journal of Advance and Applied Research S6, no. 23 (2025): 261–65. https://doi.org/10.5281/zenodo.15195212.
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<em>As digital transformation accelerates, Identity and Access Management (IAM) plays a crucial role in securing organizational resources from unauthorized access. Traditional IAM systems struggle to keep pace with increasing security threats and complexities. Artificial Intelligence (AI) has emerged as a transformative force in IAM, enhancing authentication, authorization, and anomaly detection. This paper explores the integration of AI in IAM, highlighting its ability to strengthen security by automating user authentication, detecting anomalies, and implementing adaptive access controls. AI-driven IAM solutions significantly reduce security breaches while improving user experience. This research further delves into various AI methodologies, including machine learning models, behavioral biometrics, and risk-based authentication, to understand how AI is shaping the future of IAM. Additionally, it discusses future directions in AI-driven IAM, including blockchain integration, explainable AI models, and quantum security.</em>
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Taherdoost, Hamed, Tuan-Vinh Le, and Khadija Slimani. "Cryptographic Techniques in Artificial Intelligence Security: A Bibliometric Review." Cryptography 9, no. 1 (2025): 17. https://doi.org/10.3390/cryptography9010017.
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With the rise in applications of artificial intelligence (AI) across various sectors, security concerns have become paramount. Traditional AI systems often lack robust security measures, making them vulnerable to adversarial attacks, data breaches, and privacy violations. Cryptography has emerged as a crucial component in enhancing AI security by ensuring data confidentiality, authentication, and integrity. This paper presents a comprehensive bibliometric review to understand the intersection between cryptography, AI, and security. A total of 495 journal articles and reviews were identified using Scopus as the primary database. The results indicate a sharp increase in research interest between 2020 and January 2025, with a significant rise in publications in 2023 and 2024. The key application areas include computer science, engineering, and materials science. Key cryptographic techniques such as homomorphic encryption, secure multiparty computation, and quantum cryptography have gained prominence in AI security. Blockchain has also emerged as an essential technology for securing AI-driven applications, particularly in data integrity and secure transactions. This paper highlights the crucial role of cryptography in safeguarding AI systems and provides future research directions to strengthen AI security through advanced cryptographic solutions.
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Samijonov, Nurmukhammad Y. "AI FOR INFORMATION SECURITY AND CYBERSPACE." American Journal of Applied Science and Technology 3, no. 10 (2023): 39–43. http://dx.doi.org/10.37547/ajast/volume03issue10-08.
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Taking into account the fact that artificial intelligence can expand the capabilities of humanity, accelerate the speed of data analysis several times, and help people reach correct and accurate conclusions in the decision-making process, it can improve data security or, on the contrary, target propaganda and fake news. Collecting information, taking very little responsibility, and taking into account the fact that social networks can expand the speed and extent of dissemination of information simultaneously in two opposite directions, AI can both strengthen cyber security or create new types of threats to it.
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Ryzhov, Igor, and Petro Yashchuk. "Vital security: Rethinking human security in the context of hybrid peace." Revista Amazonia Investiga 13, no. 84 (2024): 167–82. https://doi.org/10.34069/ai/2024.84.12.10.
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The study's relevance is determined by the fact that in the 21st century, the problem of ensuring the safe existence of a person has not only retained its acuteness but has also become significantly more relevant due to new challenges, dangers and threats that are becoming global. The international community and individual nation-states have yet to develop sufficient answers to these dangers. Ensuring the individual's security (vital security) about the advent of new hazards and threats to his or her essential interests entails the search for innovative ways. The study aims to systematise knowledge in personal security and to conceptualise effective measures to ensure it in the era of hybridity. The study results in formulating a vision of vital security (human security) in today's era of hybridity, particularly hybrid peace. The study's novelty is the proposed LEGO model of the "pyramid" of vital security. The LEGO model pyramid is built according to the principle similar to Maslow's pyramid but with levels of not rigid nature, which can be supplemented and rearranged according to specific (local) conditions of security landscape and security perceptions. Realist and neo-realist concepts of liberal peace have their gaps and weaknesses in the interpretation of vital security, and there is an urgent need to improve the human security paradigm, considering local specifics caused by the conditions of hybrid peace, which implies a constant state of change and reflects multi-level and multi-problematic cooperation and competition. It is shown that, instead of static-type model, inherent in previous eras, today world of hybrid peace actually represents a model of constant dynamism when all factors interact, distorting the activities of others. Human security in the era of hybridity is thus a flexible and multidimensional entity, and paradigm of its essence and functioning should be improved continuously based on Agile and systemic vision.
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IZUGBOEKWE, Chimezie Seth, Sonia Sim JOSHUA, Nasamu GAMBO, Sijibomi Victor OLUBODUN, and Blessing Onyemowo AMEH. "Artificial Intelligence and Business Security among SMEs in Abuja Metropolis." International Journal of Management Technology 11, no. 3 (2024): 17–41. https://doi.org/10.37745/ijmt.2013/vol11n31741.
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This study investigates the impact of Artificial Intelligence (AI) on business security among Small and Medium Enterprises (SMEs) in Abuja, Federal Capital Territory (FCT), Nigeria. The primary objectives are to assess the influence of AI security protocols, employee AI training, customer data privacy measures, and automated threat detection on enhancing business security. Anchored in the Socio-Technical Systems (STS) Theory, which emphasizes the interplay between social and technical elements within organizations, this research explores how these AI-driven measures collectively contribute to securing SMEs. Utilizing a cross-sectional survey design, data was collected from a representative sample of 379 employees within the Information and Communication sector, derived from an estimated population of 24,832 employees according to SMEDAN (2021). Multiple regression analysis revealed that AI security protocols, customer data privacy measures, and automated threat detection significantly enhance business security, while employee AI training showed no substantial impact. These findings underscore the necessity for integrating advanced technological measures with robust social frameworks to optimize business security. The study's results align with STS Theory, highlighting the importance of a balanced approach that incorporates both technical and social components for effective security management in SMEs.
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Samijonov, Nurmukhammad Y. "EMERGING SECURITY CONCERNS BECAUSE OF AI USAGE." Journal of Social Sciences and Humanities Research Fundamentals 3, no. 11 (2023): 43–46. http://dx.doi.org/10.55640/jsshrf-03-11-10.
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Recently, there has been a lot of hype surrounding AI, bringing urgent concerns along with lots of myth, as the future of AI is still uncertain. While social media headlines warn that AI is about to outperform humans in near future, there's a good chance that attacks made possible by the increased application of AI will be very potent, precisely targeted, hard to identify, and likely to take advantage of holes in AI systems. This article analyzes the emerging security issues that are forming in the way AI is used in practice.
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Kim, Tae Min. "Development Tasks of AI-based Security Industry." Korean Society of Private Security 23, no. 1 (2024): 181–210. http://dx.doi.org/10.56603/jksps.2024.23.1.181.
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Recently, the government's interest in industries utilizing AI has been amplified, with initiatives such as announcing a roadmap aiming to achieve the goal of becoming the world's fifth largest information security industry by 2027, with a market size of 30 trillion won, driven by digitalization and expansion in the security domain. AI represents a paradigm shift demanding innovation across nations, societies, and industries, necessitating proactive preparation and response. In the security sector, the paradigm is shifting from traditional human security to integrated security, convergence security, and AI security. To establish a foothold in the global security market, it is imperative to apply new security systems tailored to the Fourth Industrial Revolution through technological innovation, foster convergence security and physical security industries. Therefore, this study aims to propose the nurturing and development direction of the security industry based on AI, which is the core technology of the Fourth Industrial Revolution, as society evolves into a hyper-intelligent, hyper-connected, and hyper-convergent society. To foster the AI-based security industry, the application and operation of AI-based security systems need to be expanded across the entire security business. These include applying AI virtual security guards, AI-based security robots, AI-based drones, AI-based intelligent video monitoring in security rooms, AI-based autonomous driving technology and smart parking solutions, AI-based security systems in personal protection tasks, AI-based on-site crowd management systems, AI-based IoT integrated security systems, and operation of AI-based machine security systems that can be applied to the six security tasks regulated by security laws and regulations. From a policy perspective, policy support for creating new jobs and ensuring stable employment in AI security-related fields should follow. At the national level, there should be policies to nurture and support the AI-based security industry, along with efforts to address ethical issues related to AI. Furthermore, new education programs related to the Fourth Industrial Revolution and AI need to be introduced according to security laws and regulations. From a technological and academic perspective, it is necessary to enhance the professionalism of security companies to actively respond to the Fourth Industrial Revolution, and continuous development of AI-related security technologies is essential. Academic research should continue, focusing on in-depth research and development of AI security applications in various security sectors.
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Nallam, Madhuri. "AI Privacy Policies and Security." African Journal of Biological Sciences 6, Si4 (2024): 908–18. http://dx.doi.org/10.48047/afjbs.6.si4.2024.908-918.
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AI technologies often trigger concerns about privacy, a concept whose meaning can be challenging to grasp. Consequently, people’s worries about privacy tend to be unclear, complicating efforts to address these concerns and clarify how AI either poses or doesn’t pose threats to individuals. This article highlights overlooked distinctions and explored their impact on concerns about how AI technology affects privacy threats. It implies that security, rather than the fundamentals of privacy, is frequently brought up when individuals voice concerns about privacy with regard to artificial intelligence. However, the focus on security overlooks the significance of privacy in fostering autonomy and shaping our identities. Enhancing understanding of these nuances could assist AI developers in explaining to users which interests are affected and which are not using AI systems.
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Fish, Ian. "Ethics in AI Security Implications." ITNOW 60, no. 2 (2018): 34. http://dx.doi.org/10.1093/itnow/bwy046.
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Farhat, Syeda Lamima, Likhitha Tubati, Metrine Osiemo, and Rushit Dave. "AI-Based Home Security System." International Journal of Computer Science and Information Technology 16, no. 2 (2024): 37–42. http://dx.doi.org/10.5121/ijcsit.2024.16204.
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Home security is of paramount importance in today's world, where we rely more on technology, home security is crucial. Using technology to make homes safer and easier to control from anywhere is important. Home security is important for the occupant’s safety. In this paper, we came up with a low cost, AI based model home security system. The system has a user-friendly interface, allowing users to start model training and face detection with simple keyboard commands. Our goal is to introduce an innovative home security system using facial recognition technology. Unlike traditional systems, this system trains and saves images of friends and family members. The system scans this folder to recognize familiar faces and provides real-time monitoring. If an unfamiliar face is detected, it promptly sends an email alert, ensuring a proactive response to potential security threats.
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El-Hadi, Mohamed. "Generative AI Poses Security Risks." مجلة الجمعية المصرية لنظم المعلومات وتکنولوجيا الحاسبات 34, no. 34 (2024): 72–73. http://dx.doi.org/10.21608/jstc.2024.338476.
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S R, Namrutha. "AI-Driven Security Operation Center." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3152–57. https://doi.org/10.22214/ijraset.2025.68883.
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Cyber threats such as malware, phishing, and DDoS attacks are becoming increasingly sophisticated, necessitating advanced detection mechanisms. This paper presents an AI-driven cybersecurity system that integrates machine learning models for real-time detection of cyber threats, network intrusions, phishing URLs, and email phishing. The system employs NLP for malware analysis, anomaly detection for intrusion detection, and classification models for phishing prevention. Developed using FastAPI for real-time inference and SQLite for secure logging, the system ensures efficient threat identification and response. Security measures such as SQL injection protection, API authentication, and data encryption further enhance its robustness. Experimental results show high detection accuracy, with intrusion detection at 96% and email phishing detection at 97%.
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Rathod, Sachin Dashrath. "AI In Enhancing Cyber Security." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04063.
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Abstract:- Artificial intelligence( AI) is a important technology that helps cybersecurity armies automate repetitive tasks, accelerate trouble discovery and response, and meliorate the delicacy of their conduct to strengthen the security posture against various security issues and cyberattacks. This composition presents a regular literature review and a detailed analysis of AI use cases for cybersecurity provisioning. The review reacted in 2395 studies, of which 236 were linked as primary. This composition classifies the linked AI use cases predicated on a NIST cybersecurity frame using a thematic analysis approach. This type frame will give albums with a comprehensive overview of the eventuality of AI to meliorate cybersecurity in different surrounds. The review also identifies future disquisition openings in arising cybersecurity operation areas, advanced AI styles, data for representation, and the development of new infrastructures for the successful handover of AI- predicated cybersecurity in moment's period of digital transformation.
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Oseremi Onesi-Ozigagun, Yinka James Ololade, Nsisong Louis Eyo-Udo, and Damilola Oluwaseun Ogundipe. "AI-driven biometrics for secure fintech: Pioneering safety and trust." International Journal of Engineering Research Updates 6, no. 2 (2024): 001–12. http://dx.doi.org/10.53430/ijeru.2024.6.2.0023.
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AI-driven biometrics is revolutionizing the security landscape in the financial technology (FinTech) sector, enhancing safety measures and fostering trust among users. This review explores the role of AI-driven biometrics in securing FinTech operations, highlighting its benefits and implications. AI-driven biometrics refers to the use of artificial intelligence (AI) algorithms to analyze biological data for authentication purposes. This technology has gained significant traction in the FinTech sector due to its ability to provide a higher level of security than traditional authentication methods. By analyzing unique biological traits such as fingerprints, facial features, and voice patterns, AI-driven biometrics can accurately verify the identity of users, making it difficult for unauthorized individuals to gain access to sensitive financial information. One of the key benefits of AI-driven biometrics in FinTech is its ability to enhance security measures. Traditional authentication methods such as passwords and PINs are increasingly vulnerable to hacking and fraud. AI-driven biometrics, on the other hand, offers a higher level of security by using biological traits that are unique to each individual. This makes it significantly more difficult for fraudsters to gain access to sensitive financial information. In addition to enhancing security, AI-driven biometrics also improves the user experience. By replacing traditional authentication methods with biometric authentication, users can access their accounts more quickly and conveniently, without the need to remember complex passwords or PINs. This not only improves the overall user experience but also reduces the risk of user error and account lockouts. Overall, AI-driven biometrics is pioneering safety and trust in the FinTech sector by providing a higher level of security and enhancing the user experience. As this technology continues to evolve, it is likely to play an increasingly important role in securing financial transactions and fostering trust among users.
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Naveen Kumar Birru. "Secure AI Infrastructure: Building Trustworthy AI Systems in Distributed Environments." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2756–67. https://doi.org/10.30574/wjaets.2025.15.2.0748.
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As enterprises increasingly deploy artificial intelligence to drive customer experiences, business intelligence, and automation, ensuring the security of AI infrastructure has become paramount. Distributed AI systems must not only be scalable and performant they must also be trustworthy, protecting sensitive data and model integrity across dynamic, cloud-native environments. This article explores critical components of secure AI infrastructure, highlighting strategies and technologies for building resilient systems that withstand sophisticated threats. From securing data pipelines with encryption and access controls to protecting model training environments and inference endpoints, a comprehensive defense-in-depth approach addresses the unique security challenges of AI systems. Privacy-preserving techniques like federated learning and differential privacy enable organizations to balance utility with data protection requirements. Proper governance frameworks incorporating model inventories, version control, and ethical considerations establish the foundation for responsible AI deployment. Through practical implementation examples, including a case study from the financial services sector, this article demonstrates how organizations can create AI systems that protect against emerging threats while maintaining operational effectiveness across diverse computing environments.
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Bin Muhammad, Mohd Hilal, Zulhazlin Bin Abas, Anas Suzastri Bin Ahmad, and Mohd Sufyan Bin Sulaiman. "AI-Driven Security: Redefining Security Informations Systems within Digital Governance." International Journal of Research and Innovation in Social Science VIII, no. IX (2024): 2923–36. http://dx.doi.org/10.47772/ijriss.2024.8090245.
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The increasing integration of Artificial Intelligence (AI) within Security Information Systems (SIS) presents a significant shift in digital governance, where governments rely heavily on secure digital infrastructures to manage public services. The escalating threat landscape has necessitated a proactive approach to cybersecurity, and AI is proving crucial in enhancing threat detection, automating responses, and minimizing human error. However, many governments, particularly in developing nations, are struggling to bridge the gap between their current security measures and the complex challenges posed by sophisticated cyber threats. This study aims to explore how AI can redefine SIS in digital governance by improving national resilience and addressing the gaps in traditional security protocols. The study employs a systematic literature review methodology, examining recent research to analyze AI’s role in enhancing SIS, with a particular focus on machine learning, deep learning, and adaptive security measures. Findings indicate that AI-driven security significantly enhances the speed and accuracy of threat detection, providing dynamic solutions that continuously adapt to evolving threats. Nonetheless, the study also highlights concerns around ethical governance, data privacy, and transparency, pointing to the need for robust regulatory frameworks to govern AI’s deployment in public sector security systems. The implications of this research are twofold: theoretically, it contributes to the broader understanding of AI’s role in cybersecurity resilience; practically, it offers insights for policymakers aiming to integrate AI into their governance strategies. The study concludes by recommending further empirical research, particularly in the context of developing nations, where AI-driven security solutions are needed to enhance national cybersecurity frameworks and protect critical public infrastructures.
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Raakesh Dhanasekaran. "Generative AI Integration with Cloud Services: Revolutionizing Cybersecurity Frameworks." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1619–25. https://doi.org/10.30574/wjaets.2025.15.3.1086.
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The integration of generative artificial intelligence with cloud computing has fundamentally transformed cybersecurity frameworks, enabling unprecedented capabilities in threat detection and automated incident response. This technological convergence allows organizations to shift from reactive to proactive security postures through sophisticated anomaly detection and predictive analytics. Major cloud providers have embedded AI-driven security tools that analyze vast datasets to identify subtle patterns indicative of potential threats before they materialize into breaches. While delivering significant improvements in detection accuracy, response time, and cost reduction, this integration also introduces novel security challenges. Adversarial attacks against AI models, AI-generated phishing campaigns, and automated malware represent emerging threats that require comprehensive countermeasures. Multi-layered security frameworks incorporating access control, data protection, confidential computing, model security, and continuous monitoring provide effective defense mechanisms. Confidential computing emerges as a critical technology for securing AI operations, protecting sensitive data during processing through hardware-based isolation while facilitating secure multi-party computation for collaborative model training across regulated industries. The rapid evolution of this technological intersection demands ongoing adaptation of security strategies and governance frameworks to ensure that organizations can leverage the transformative potential of AI while maintaining robust defenses against increasingly sophisticated threat actors targeting the convergence of AI and cloud infrastructure.
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Kolade, Titilayo Modupe, Nsidibe Taiwo Aideyan, Seun Michael Oyekunle, Olumide Samuel Ogungbemi, Dooshima Louisa Dapo-Oyewole, and Oluwaseun Oladeji Olaniyi. "Artificial Intelligence and Information Governance: Strengthening Global Security, through Compliance Frameworks, and Data Security." Asian Journal of Research in Computer Science 17, no. 12 (2024): 36–57. https://doi.org/10.9734/ajrcos/2024/v17i12528.
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This study examines the dual role of artificial intelligence (AI) in advancing and challenging global information governance and data security. By leveraging methodologies such as Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and Multi-Criteria Decision Analysis (MCDA), the study investigates AI-specific vulnerabilities, governance gaps, and the effectiveness of compliance frameworks. Data from the MITRE ATT&CK Framework, AI Incident Database, Global Cybersecurity Index (GCI), and National Vulnerability Database (NVD) form the empirical foundation for this analysis. Key findings reveal that AI-driven data breaches exhibit the highest regulatory scores (0.72) and dependency levels (0.81), underscoring the critical need for robust compliance frameworks in high-risk AI environments. PCA identifies regulatory gaps (45.3% variance) and AI technology type (30.2% variance) as significant factors influencing security outcomes. SEM highlights governance strength as a primary determinant of security effectiveness (coefficient = 0.68, p < 0.001), while MCDA underscores the importance of adaptability in governance frameworks for addressing AI-specific threats. The study recommends adopting quantum-resistant encryption, enhancing international cooperation, and integrating AI automation with human oversight to fortify governance structures. These insights provide actionable strategies for policymakers, industry leaders, and researchers to navigate the complexities of AI governance and align technological advancements with ethical and security imperatives in a rapidly evolving digital landscape.
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Phijik, Dr B. "Blockchain And AI in Healthcare Data Security." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50040.
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Abstract— Data security and integrity are two essential concepts in the modern era but there are some sectors, which are more sensitive when controlling data. With this project, “Securing Data with Blockchain and AI”, it is possible to develop efficient data management by incorporating blockchain and artificial intelligence technology. The healthcare domain in particular is highlighted to demonstrate the practical use of the project focusing on the protection of patient’s data. Every transaction creates a hash that is attached to the transaction. Some of the notable features included: hospital and patient logins, patient registration, blockchain hashes displayed during logins, AI search feature for easy access of data. Some of the security implementations include, Secure Hash Algorithm 256 (SHA256), and Advanced Encryption Standard (AES), which will be used to encrypt sensitive data. Keywords: Data Security, Data Systems, Artificial Intelligence.
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Venkadesh, Dr P. "Aegis AI - Intelligent Cyber Resilience." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42978.
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As cyber threats continue to evolve in complexity and scale, traditional security measures have become insufficient. Aegis AI (AAI): Intelligent Cyber Resilience presents a cutting-edge approach that integrates artificial intelligence (AI) and machine learning (ML) to strengthen cybersecurity defenses. This study explores the role of AI-driven threat intelligence, automated incident response, and adaptive learning in combating cyberattacks. The proposed AAI framework utilizes deep learning, anomaly detection, and reinforcement learning techniques to predict and mitigate threats in real time. By enhancing cyber resilience, AAI reduces response times, minimizes false positives, and ensures robust security automation. The research also addresses adversarial machine learning risks and ethical concerns surrounding AI in cybersecurity. Our findings demonstrate that AI-powered security systems significantly improve detection accuracy and automate cyber defense strategies. Future research will focus on integrating federated learning, real-time behavioural analytics, and AI-driven compliance frameworks to further enhance AAI’s effectiveness in securing digital ecosystems. Aegis AI is an advanced AI-driven cybersecurity framework designed to enhance cyber resilience by integrating intelligent threat detection, automated incident response, and adaptive learning. It leverages machine learning models to identify, predict, and mitigate cyber threats in real time, reducing response times and minimizing security breaches. By continuously updating its knowledge through federated learning and behavioural analytics, Aegis AI ensures proactive defense against evolving cyber threats, making digital ecosystems more secure and resilient. Aegis AI transforms cybersecurity by providing an intelligent, self-adaptive defense system that not only detects and mitigates threats in real time but also evolves with emerging cyber risks. By automating security responses and continuously learning from new threats, Aegis AI ensures a resilient, proactive, and future-proof cybersecurity framework, safeguarding digital assets with unmatched efficiency. Keywords: AI in Cybersecurity, Threat Intelligence, Machine Learning, Cyber Resilience, Automated Incident
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Kharbanda, Varun, Seetharaman A, and Maddulety K. "Journal." International Journal of Security and Privacy in Pervasive Computing 15, no. 1 (2023): 1–13. http://dx.doi.org/10.4018/ijsppc.318676.
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Artificial intelligence (AI) has emerged as the most widely applicable field across varied industries. Being an evolving technology, it may be quite useful in sensitive areas such as cyber security where there is a dire need for implementation of AI technologies, such as expert systems, neural networks, intelligent agents, and artificial immune systems. The primary reason for AI fitment to cyber security area is its ability to detect anomalies proactively and predictively in the network, thereby working towards securing the network before the damage related to loss of data and/or reputation is done. There are different types of AI technologies as mentioned above that could be applied in cyber security in its varied forms. In this paper, the emphasis is on specific AI technologies that can bring unique benefits to the cyber security field with its unique applicability to different scenarios. The outcome of this study shows that AI technologies such as expert systems, neural networks, intelligent agents, and artificial immune systems are transforming the landscape for managing cyber threats.
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Lee, Jungchan, Cheolhee Yoon, and Jinyoung Choi. "Research on Implementing Security Governance for SDV AI Security." Journal of Korean Institute of Information Technology 23, no. 4 (2025): 203–11. https://doi.org/10.14801/jkiit.2025.23.4.203.
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Varadhan Krishnan, Varadharaj. "Scaling Security Incident Response with Generative AI." International Journal of Science and Research (IJSR) 13, no. 9 (2024): 808–12. http://dx.doi.org/10.21275/sr24913085010.
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Ourzik, Victoria Yousra. "Security and safety concerns in the age of AI." International Conference on AI Research 4, no. 1 (2024): 329–37. https://doi.org/10.34190/icair.4.1.3142.
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Artificial Intelligence (AI) is transforming industries at an astonishing rate, reshaping how we live, work, and interact with technology. Yet, as AI becomes more pervasive, it brings urgent questions about security and safety. This article explores these critical issues, drawing a clear distinction between AI security and AI safety—two concepts that are often misunderstood but are crucial for responsible AI deployment. AI security focuses on protecting systems from external threats like data breaches, adversarial attacks, and unauthorized access. As AI systems increasingly handle sensitive data and control critical operations, securing them against such risks is essential. A breach or failure could compromise not only privacy but also the integrity of critical infrastructures. On the other hand, AI safety extends beyond technical defenses to the broader societal implications of AI. Issues like algorithmic bias, ethical decision-making, and unintended consequences of AI systems highlight the risks to human well-being. As AI becomes more autonomous, its alignment with human values and societal norms becomes paramount. Furthermore, the existential risks posed by advanced AI—such as loss of control or unintended outcomes—raise profound questions about the future of human-AI coexistence. This article delves into real-world case studies of AI failures and near-misses, offering tangible insights into the potential consequences of unchecked AI growth. It also explores strategies for mitigating these risks, balancing the pursuit of innovation with the need for transparency, accountability, and ethical oversight. As we look to the future, international cooperation and robust regulatory frameworks are essential to managing AI’s growing influence. By examining both technical and ethical dimensions, this article equips readers with a comprehensive understanding of AI security and safety, urging a proactive approach to managing the risks and harnessing the potential of this powerful technology.
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Chernysh, Roman, Mariia Chekhovska, Olena Stoliarenko, Olena Lisovska, and Andrii Lyseiuk. "Ensuring information security of critical infrastructure objects as a component to guarantee Ukraine’s national security." Revista Amazonia Investiga 12, no. 67 (2023): 87–95. http://dx.doi.org/10.34069/ai/2023.67.07.8.
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The purpose of the article is to define and justify the conceptual foundations of the implementation of the state policy on ensuring the information security of critical infrastructure objects, as a component of guaranteeing the national security of Ukraine. The methodological basis of the study was a set of general scientific and special methods of scientific knowledge: dialectical-phenomenological, systemic analysis and synthesis, structural-functional, deduction and induction, etc. According to the results of the research: the author's definition of the concepts «information security of critical infrastructure objects of Ukraine» and «threats to information security of critical infrastructure objects of Ukraine» was formulated; the elements of the system of threats to the information security of critical infrastructure objects of Ukraine are identified; the factors leading to the emergence of threats to critical infrastructure objects of Ukraine are singled out and groups of basic measures aimed at countering traditional and new threats to the information security of critical infrastructure objects of Ukraine are formulated.
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Shinde, Rucha, Shruti Patil, Ketan Kotecha, and Kirti Ruikar. "Blockchain for Securing AI Applications and Open Innovations." Journal of Open Innovation: Technology, Market, and Complexity 7, no. 3 (2021): 189. http://dx.doi.org/10.3390/joitmc7030189.
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Nowadays, open innovations such as intelligent automation and digitalization are being adopted by every industry with the help of powerful technology such as Artificial Intelligence (AI). This evolution drives systematic running processes, involves less overhead of managerial activities and increased production rate. However, it also gave birth to different kinds of attacks and security issues at the data storage level and process level. The real-life implementation of such AI-enabled intelligent systems is currently plagued by the lack of security and trust levels in system predictions. Blockchain is a prevailing technology that can help to alleviate the security risks of AI applications. These two technologies are complementing each other as Blockchain can mitigate vulnerabilities in AI, and AI can improve the performance of Blockchain. Many studies are currently being conducted on the applicability of Blockchains for securing intelligent applications in various crucial domains such as healthcare, finance, energy, government, and defense. However, this domain lacks a systematic study that can offer an overarching view of research activities currently going on in applying Blockchains for securing AI-based systems and improving their robustness. This paper presents a bibliometric and literature analysis of how Blockchain provides a security blanket to AI-based systems. Two well-known research databases (Scopus and Web of Science) have been examined for this analytical study and review. The research uncovered that idea proposals in conferences and some articles published in journals make a major contribution. However, there is still a lot of research work to be done to implement real and stable Blockchain-based AI systems.
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Sarku, Rebecca, Ulfia A. Clemen, and Thomas Clemen. "The Application of Artificial Intelligence Models for Food Security: A Review." Agriculture 13, no. 10 (2023): 2037. http://dx.doi.org/10.3390/agriculture13102037.
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Emerging technologies associated with Artificial Intelligence (AI) have enabled improvements in global food security situations. However, there is a limited understanding regarding the extent to which stakeholders are involved in AI modelling research for food security purposes. This study systematically reviews the existing literature to bridge the knowledge gap in AI and food security, focusing on software modelling perspectives. The study found the application of AI models to examine various indicators of food security across six continents, with most studies conducted in sub-Saharan Africa. While research organisations conducting AI modelling were predominantly based in Europe or the Americas, their study communities were in the Global South. External funders also supported AI modelling research on food security through international universities and research institutes, although some collaborations with local organisations and external partners were identified. The analysis revealed three patterns in the application of AI models for food security research: (1) the exclusive utilisation of AI models to assess food security situations, (2) stakeholder involvement in some aspects of the AI modelling process, and (3) stakeholder involvement in AI modelling for food security through an iterative process. Overall, studies on AI models for food security were primarily experimental and lacked real-life implementation of the results with stakeholders. Consequently, this study concluded that research on AI, which incorporates feedback and/or the implementation of research outcomes for stakeholders, can contribute to learning and enhance the validity of the models in addressing food security challenges.
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Lisovskyi, Kostiantyn, and Gleb-David Rochenovich. "ARTIFICIAL INTELLIGENCE IN THE SECURITY SYSTEM OF ENTERPRISE." Grail of Science, no. 27 (May 25, 2023): 308–16. http://dx.doi.org/10.36074/grail-of-science.12.05.2023.049.
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Artificial intelligence (AI) is transforming a number of business functions, including enterprise security systems. Recent years have seen an increase in the use of AI in security systems to detect and mitigate security threats. The purpose of this study is to investigate the use of AI in enterprise security systems. The study aims to identify the novelties in the application of AI to enterprise security systems, the research voids in the existing literature, and the benefits of AI to enterprise security systems. Existing research on the applicability of AI in enterprise security systems will be analyzed through a literature review methodology. The study will shed light on the use of artificial intelligence in enterprise security systems and its future implications.
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Nycz, Mariusz, Mirosław Hajder, and Sara Nienajadlo. "Methods for increasing security of web servers." Annales Universitatis Mariae Curie-Sklodowska, sectio AI – Informatica 16, no. 2 (2017): 39. http://dx.doi.org/10.17951/ai.2016.16.2.39.
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<p>This article is addressed in most part to people dealing with security of web servers. This paper begins with presenting the statistical dimension of the issue of data security in the modern Internet. This paper begins with presenting statistics dealing with issues of data security on the modern World Wide Web. The authors main focus in this work is presenting the challenges of dealing with security and protection of web communication. The work analyses the security of implementing SSL/TLS (Secure Socket Layer/Transport Layer Security) protocol and proposes a new method of increasing security of web servers. This article is addressed to people dealing with analysis and security of web servers.</p>
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Isabirye Edward Kezron. "Securing the AI supply chain: Mitigating vulnerabilities in AI model development and deployment." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 2336–46. https://doi.org/10.30574/wjarr.2024.22.2.1394.
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The rapid advancement and integration of Artificial Intelligence (AI) across critical sectors — including healthcare, finance, defense, and infrastructure — have exposed an often-overlooked risk: vulnerabilities within the AI supply chain. This research examines the security challenges and potential threats affecting AI model development and deployment, focusing on adversarial attacks, data poisoning, model theft, and compromised third-party components. By dissecting the AI supply chain into its core stages — data sourcing, model training, deployment, and maintenance — this study identifies key entry points for malicious actors. The paper proposes a multi-layered security framework combining blockchain-based data provenance, federated learning for decentralized model training, and zero-trust architecture to ensure secure deployment. Additionally, it explores how adversarial training, model watermarking, and real-time anomaly detection can mitigate risks without sacrificing model performance. Case studies of high-profile AI breaches are analyzed to demonstrate the consequences of unsecured pipelines, emphasizing the urgency of securing AI systems.
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Bourair Al-Attar. "Network Security in AI-based healthcare systems." Babylonian Journal of Networking 2023 (November 30, 2023): 112–24. https://doi.org/10.58496/bjn/2023/015.
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With the fast integration of artificial intelligence (AI) in healthcare, boosting diagnostics, treatment tailoring, and predictive analytics, securing patient data, and ensuring system integrity have become key challenges. This research examines the network security concerns particular to AI-based healthcare systems, attempting to uncover main vulnerabilities and assess viable protection strategies. Through a mix of systematic review and experimental validation, we tested numerous machines learning models, including convolutional neural networks (CNN), support vector machines (SVM), and random forests, against adversarial assaults that undermine model accuracy and data privacy. Results demonstrated that adversarial attacks might considerably impair model dependability, with accuracy decreases of up to 32% in CNN models under assault. However, adopting defensive strategies like adversarial training and defensive distillation dramatically increased model resilience, with post-defense accuracy rates returning by 15-25%. These results underline the necessity for strong network security policies suited to AI healthcare applications to guarantee both data protection and operational reliability. Our work adds useful insights on the adaptation of AI network security measures inside healthcare, identifying avenues for legislative updates and ongoing research to safeguard upcoming AI-driven health advances.
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Burdina, Anna A., Anna V. Bondarenko, Nataliya V. Moskvicheva, and Narmina O. Melik-Aslanova. "Management of complex economic security of enterprises: empirical test in Russia." Revista Amazonia Investiga 10, no. 46 (2021): 303–10. http://dx.doi.org/10.34069/ai/2021.46.10.30.
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There is a new meaningful interpretation of the concept of "integrated economic security of industrial enterprise"; the problems of analyzing complex economic security are substantiated. Based on the study of the regulatory and legal and methodological support of the process of managing economic security in modern economic and political conditions, generalizations and analysis of existing practice in Russia and abroad, the main shortcomings in approaches to assessing the integrated economic security of industrial enterprises have been identified. The article examines the methods for assessing economic security: matrix, product, operational, dynamic one and methods of assessment based on the market value of the company. The parameters of the integrated economic security of industrial enterprise are classified: financial and economic security, information, technological, material and technical, personnel, epidemiological security, conceptual model for assessing financial and economic and integrated security of enterprise has been developed. The practical implementation of the proposed developments has been carried out. It is recommended to use the developed model of economic security management as enterprise resource planning (ERP) module of the enterprise system.
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Grosse, Kathrin, Lukas Bieringer, Tarek R. Besold, Battista Biggio, and Alexandre Alahi. "When Your AI Becomes a Target: AI Security Incidents and Best Practices." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23041–46. http://dx.doi.org/10.1609/aaai.v38i21.30347.
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In contrast to vast academic efforts to study AI security, few real-world reports of AI security incidents exist. Released incidents prevent a thorough investigation of the attackers' motives, as crucial information about the company and AI application is missing. As a consequence, it often remains unknown how to avoid incidents. We tackle this gap and combine previous reports with freshly collected incidents to a small database of 32 AI security incidents. We analyze the attackers' target and goal, influencing factors, causes, and mitigations. Many incidents stem from non-compliance with best practices in security and privacy-enhancing technologies. In the case of direct AI attacks, access control may provide some mitigation, but there is little scientific work on best practices. Our paper is thus a call for action to address these gaps.
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Pidbereznykh, Inna, Oleg Koval, Yevhen Solomin, Vitaliy Kryvoshein, and Tetyana Plazova. "Ukrainian policy in the field of information security." Revista Amazonia Investiga 11, no. 60 (2022): 206–13. http://dx.doi.org/10.34069/ai/2022.60.12.22.
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The article analyzes the modern challenges of the time, which shape the information security policy of Ukraine. The paper analyzes the approaches to the definition of information security to understand this concept. Conclusions are made that information security is a constant movement, changeable, versatile concept, which cannot be stable. The article studies the information security of Ukraine as a component in the system of international information security. It is established that in the conditions of war, the role of information security of Ukraine in the international community has sharply increased. Ukraine's policy in the sphere of economic security is clearly marked in its legislation and meets the challenges of our time. The article presents a list of the main threats to the information security of the country: threats to independence and sovereignty through hybrid, information warfare by the aggressor state Russia, threats in the systems of the interaction of state bodies, threats related to the media, threats due to the lack of awareness and culture of information security among the population. Conclusions are made that future cases in the field of information security will be related to the elimination of threats. A list of goals for achieving information security is presented.
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Vereshchak, Vasyl, Bohdan Holjanych, Kostiantyn Mamchur, Hlib Smoliar, and Pavlo Terpiak. "Regional (continental) security: emphases of 2022." Revista Amazonia Investiga 11, no. 54 (2022): 30–40. http://dx.doi.org/10.34069/ai/2022.54.06.3.
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The intensification of the processes of globalization, regionalization, geopoliticization and the desire of countries to integrate into the world financial, economic and social-political space lead to the emergence of new challenges and dangers and intensification of existing ones in the field of regional political and security cooperation. The strengthening of military activity on the territory of sovereign countries, the lack of a peaceful settlement of regional conflict situations, and the growth of tension and instability require regional cooperation in the fight against common threats on the basis of ensuring the implementation of the principles of international law. Regarding the results of the research on the features of ensuring regional (continental) security under the influence of challenges and threats in 2022, it has been established that there are four groups from among the countries of the world that are characterized by common features of ensuring regional (continental) security, namely: highly developed countries that are able to ensure a high level of security, the efficiency of its management and the stability of state governance; countries with a relatively high level of development that ensure high standards of regional (continental) security, however, significant efforts are directed to solving security issues; countries with an intermediate level of development that are significantly influenced by other countries and are in a state of struggle for the redistribution of spheres of influence; countries with a low level of development that have not completed the process of transformational restructuring and require strengthened measures in order to ensure regional (continental) security.
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Hu, Yupeng, Wenxin Kuang, Zheng Qin, et al. "Artificial Intelligence Security: Threats and Countermeasures." ACM Computing Surveys 55, no. 1 (2023): 1–36. http://dx.doi.org/10.1145/3487890.
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In recent years, with rapid technological advancement in both computing hardware and algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human being in a wide range of fields, such as image recognition, education, autonomous vehicles, finance, and medical diagnosis. However, AI-based systems are generally vulnerable to various security threats throughout the whole process, ranging from the initial data collection and preparation to the training, inference, and final deployment. In an AI-based system, the data collection and pre-processing phase are vulnerable to sensor spoofing attacks and scaling attacks, respectively, while the training and inference phases of the model are subject to poisoning attacks and adversarial attacks, respectively. To address these severe security threats against the AI-based systems, in this article, we review the challenges and recent research advances for security issues in AI, so as to depict an overall blueprint for AI security. More specifically, we first take the lifecycle of an AI-based system as a guide to introduce the security threats that emerge at each stage, which is followed by a detailed summary for corresponding countermeasures. Finally, some of the future challenges and opportunities for the security issues in AI will also be discussed.
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Sunday Adeola Oladosu, Adebimpe Bolatito Ige, Christian Chukwuemeka Ike, Peter Adeyemo Adepoju, Olukunle Oladipupo Amoo, and Adeoye Idowu Afolabi. "AI-driven security for next-generation data centers: Conceptualizing autonomous threat detection and response in cloud-connected environments." GSC Advanced Research and Reviews 15, no. 2 (2023): 162–72. https://doi.org/10.30574/gscarr.2023.15.2.0136.
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The dynamic evolution of next-generation data centers, driven by cloud-native and hybrid architectures, has necessitated a paradigm shift in cybersecurity. Traditional security models, designed for static and on-premise environments, struggle to address the complexities of cloud-connected infrastructures and the rapidly evolving threat landscape. Emerging challenges, such as advanced persistent threats (APTs), ransomware, and insider attacks, demand sophisticated and adaptive security solutions. In this context, artificial intelligence (AI) emerges as a transformative technology capable of redefining threat detection and response mechanisms. This review explores the conceptualization of AI-driven security for next-generation data centers, focusing on autonomous threat detection and response. By leveraging AI and machine learning (ML), security systems can achieve real-time anomaly detection, advanced behavior analysis, and predictive risk assessment. These capabilities enhance the accuracy and speed of identifying malicious activities while reducing false positives. Additionally, autonomous response mechanisms, such as self-healing networks and adaptive security policies, enable rapid containment and mitigation of threats, minimizing potential damages. The review also discusses the integration of AI with existing Security Operations Centers (SOCs), highlighting its potential to augment human decision-making and automate repetitive tasks. Furthermore, it examines the role of advanced encryption, identity management, and compliance tools in fortifying security frameworks. Future trends, including the impact of 5G and edge computing, are explored, emphasizing their implications for real-time applications and IoT security. This study underscores the importance of proactive, AI-driven strategies in securing next-generation data centers, ensuring scalability, resilience, and robust protection in an increasingly interconnected digital landscape. By bridging the gap between cloud-native and on-premise environments, AI-powered security frameworks offer a promising path toward achieving autonomous, adaptive, and future-proof cybersecurity.
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Akhtar, Zarif Bin, and Ahmed Tajbiul Rawol. "Enhancing Cybersecurity through AI-Powered Security Mechanisms." IT Journal Research and Development 9, no. 1 (2024): 50–67. http://dx.doi.org/10.25299/itjrd.2024.16852.
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In the rapidly evolving landscape of digital technology, the proliferation of interconnected systems has brought unprecedented opportunities and challenges. Among these challenges, the escalating frequency and sophistication of cyberattacks pose significant threats to individuals, organizations, and nations. In response, the fusion of Cybersecurity and Artificial Intelligence (AI) has emerged as a pivotal paradigm, offering proactive, intelligent, and adaptable defense mechanisms. This research explores the transformative impacts of AI-powered security on cybersecurity, demonstrating how AI techniques, including machine learning, natural language processing, and anomaly detection, fortify digital infrastructures. By analyzing vast volumes of data at speeds beyond human capacity, AI-driven cybersecurity systems can identify subtle patterns indicative of potential threats, allowing for early detection and prevention. The exploration consolidates existing studies, highlighting the trends and gaps that this research addresses. Expanded results and discussions provide a detailed analysis of the practical benefits and challenges of AI applications in cybersecurity, including case studies that offer concrete evidence of AI's impact. Novel contributions are emphasized through comparisons with other studies, showcasing improvements in accuracy, precision, recall, and F-score metrics, which demonstrate the effectiveness of AI in enhancing cybersecurity measures. The synergy between AI and human expertise is explored, highlighting how AI-driven tools augment human analysts' capabilities. Ethical considerations and the "black box" nature of AI algorithms are addressed, advocating for transparent and interpretable AI models to foster trust and collaboration between man and machine. The challenges posed by adversarial AI, where threat actors exploit AI system vulnerabilities, are examined. Strategies for building robust AI security mechanisms, including adversarial training, model diversification, and advanced threat modeling, are discussed. The research also emphasizes a holistic approach that combines AI-driven automation with human intuition and domain knowledge. As AI continues to rapidly evolve, a proactive and dynamic cybersecurity posture can be established, bolstering defenses, mitigating risks, and ensuring the integrity of our increasingly interconnected digital world.