Unmasking Vulnerabilities: AI-Powered Cybersecurity Threats and Their Impact on National Security
Exploring the Dual Role of AI in Modern Cybersecurity- A Threat and a Shield
Keywords:
Artificial Intelligence, Cybersecurity, AI-Powered Cyberattacks, Predictive Threat Modeling, Automated Incident Response, Ethical AI GovernanceAbstract
Artificial intelligence (AI) is redefining the cybersecurity world as a force for both a strong defense and an ever more sophisticated attack. This article critically examines AI with respect to the dual role it serves regarding recent breaches, AI’s development as a tool for developing AI powered threats, and advancing AI powered defensive systems. Increasing reliance on AI driven tools by cybercriminals means high profile incidents like the Microsoft breach will continue. AI powered defense mechanisms, like behavioral analytics, predictive modeling, and automated response to incident, are increasing their utility as weapons against risks at the same time. AI is a dual use technology with ethical and practical challenges such as accessibility of malicious actors, accountability of implementation and widening gap between the resource rich and resource poor organizations. The article emphasizes the importance of doing global collaboration, additive AI on zero trust architecture and the regulations around responsible innovation. In this work, we provide a critical analysis on AI’s abilities and the limitations it presents; highlighting the need to use AI responsibly and accordingly in the case of such risks embedded in the AI itself. To achieve it, governments, organizations and developers all need to work together to create adaptable systems that will navigate through continuously changing threat landscapes.
References
Ajala, O. A., Okoye, C. C., Ofodile, O. C., Arinze, C. A., & Daraojimba, O. D. (2024). Review of AI and machine learning applications to predict and Thwart cyber-attacks in real-time. Magna Scientia Advanced Research and Reviews, 10(1), 312-320.
Aslan, Ö., Aktuğ, S. S., Ozkan-Okay, M., Yilmaz, A. A., & Akin, E. (2023). A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions. Electronics, 12(6), 1333. https://www.mdpi.com/2079-9292/12/6/1333
Azad, M. A., Abdullah, S., Arshad, J., Lallie, H., & Ahmed, Y. H. (2024). Verify and trust: A multidimensional survey of zero-trust security in the age of IoT. Internet of Things, 27, 101227. https://www.sciencedirect.com/science/article/pii/S2542660524001689
Baki, S., Verma, R. M., Mukherjee, A., & Gnawali, O. (2020). Less is more: Exploiting social trust to increase the effectiveness of a deception attack. arXiv preprint arXiv:2006.13499. https://arxiv.org/abs/2006.13499
Balantrapu, S. S. (2024). AI for Predictive Cyber Threat Intelligence. International Journal of Management Education for Sustainable Development, 7(7), 1-28.
Bechara, F. R., & Schuch, S. B. (2021). Cybersecurity and global regulatory challenges. Journal of Financial Crime, 28(2), 359-374. https://www.emerald.com/insight/content/doi/10.1108/JFC-07-2020-0149/full/html
Beg, R., Bhardwaj, V., Kumar, M., Muzumdar, P., Rajput, A., & Borana, K. (2024). Unmasking Social Media Crimes: Types, Trends, and Impact. Online Social Networks in Business Frameworks, 1-26. https://onlinelibrary.wiley.com/doi/abs/10.1002/9781394231126.ch1
Camacho, N. G. (2024). The Role of AI in Cybersecurity: Addressing Threats in the Digital Age. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 3(1), 143-154.
Chaudhry, U. B., & Hydros, A. K. (2023). Zero‐trust‐based security model against data breaches in the banking sector: A blockchain consensus algorithm. IET blockchain, 3(2), 98-115. https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/blc2.12028
Checkpoint (2023). Check Point Research Reports a 38% Increase in 2022 Global Cyberattacks. Retrieved from: https://Check Point Research Reports a 38% Increase in 2022 Global Cyberattacks - Check Point Blog
Chowdhury, R. H., Prince, N. U., Abdullah, S. M., & Mim, L. A. (2024). The role of predictive analytics in cybersecurity: Detecting and preventing threats. World Journal of Advanced Research and Reviews, 23(2), 1615-1623.
CNET. (2023). It's scary easy to use ChatGPT to write phishing emails. Retrieved from https://www.cnet.com
Dhoni, P., & Kumar, R. (2023). Synergizing generative ai and cybersecurity: Roles of generative ai entities, companies, agencies, and government in enhancing cybersecurity. Authorea Preprints. https://www.techrxiv.org/doi/full/10.36227/techrxiv.23968809.v1
Fakhouri, H. N., Alhadidi, B., Omar, K., Makhadmeh, S. N., Hamad, F., & Halalsheh, N. Z. (2024, February). AI-Driven Solutions for Social Engineering Attacks: Detection, Prevention, and Response. In 2024 2nd International Conference on Cyber Resilience (ICCR) (pp. 1-8). IEEE. https://ieeexplore.ieee.org/abstract/document/10533010/
Feng, X., Feng, Y., & Dawam, E. S. (2020, August). Artificial intelligence cyber security strategy. In 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) (pp. 328-333). IEEE.
Guembe, B., Azeta, A., Misra, S., Osamor, V. C., Fernandez-Sanz, L., & Pospelova, V. (2022). The emerging threat of ai-driven cyber attacks: A review. Applied Artificial Intelligence, 36(1), 2037254. https://www.tandfonline.com/doi/abs/10.1080/08839514.2022.2037254
HIPAA Journal. (2023). 98% of organizations use a vendor that had a data breach in the past 2 years. Retrieved from https://www.hipaajournal.com/98-pc-organizations-vendor-data-breach/
IBM. (2024a). Cost of a data breach report 2024. Retrieved from https://www.ibm.com/reports/data-breach
IBM. (2024b). X-Force Threat Intelligence Index 2024. Retrieved from https://www.ibm.com/reports/threat-intelligence
Jimmy, F. N. U. (2024). Cyber security Vulnerabilities and Remediation Through Cloud Security Tools. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 2(1), 129-171. https://ojs.boulibrary.com/index.php/JAIGS/article/view/102
Kaloudi, N., & Li, J. (2020). The ai-based cyber threat landscape: A survey. ACM Computing Surveys (CSUR), 53(1), 1-34. https://dl.acm.org/doi/abs/10.1145/3372823
Karthika, C. V., Adhavan, N., & Pooja, S. J. (2023). Navigating the Perspective of Artificial Intelligence and Cybersecurity: Grabbing Opportunities amidst Ground Breaking Challenges. Issue 4 Int'l JL Mgmt. & Human., 6, 2702. https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/ijlmhs24§ion=237
Khan, O. U., Abdullah, S. M., Olajide, A. O., Sani, A. I., Faisal, S. M. W., Ogunola, A. A., & Lee, M. D. (2024). The Future of Cybersecurity: Leveraging Artificial Intelligence to Combat Evolving Threats and Enhance Digital Defense Strategies. Journal of Computational Analysis and Applications, 33(8).
Kumar, M., Darshan, S. S., & Yarlagadda, V. (2023). Introduction to the cyber-security landscape. In Malware Analysis and Intrusion Detection in Cyber-Physical Systems (pp. 1-21). IGI Global. https://www.igi-global.com/chapter/introduction-to-the-cyber-security-landscape/331297
Kumar, S. U. B. O. D. H., Athavale, V. A., & Kartikey, D. I. V. Y. E. (2021). Security issues in cloud computing: A holistic view. International Journal of Internet of Things and Web Services, 6, 18-29. https://www.iaras.org/iaras/filedownloads/ijitws/2021/022-0003(2021).pdf
Kumari, S. (2022). Cybersecurity in Digital Transformation: Using AI to Automate Threat Detection and Response in Multi-Cloud Infrastructures. Journal of Computational Intelligence and Robotics, 2(2), 9-27.
Liu, M., Zhang, Y., Liu, B., Li, Z., Duan, H., & Sun, D. (2021). Detecting and characterizing SMS spearphishing attacks. In Proceedings of the 37th Annual Computer Security Applications Conference (pp. 930-943).
Malatji, M., & Tolah, A. (2024). Artificial intelligence (AI) cybersecurity dimensions: a comprehensive framework for understanding adversarial and offensive AI. AI and Ethics, 1-28.
Mallick, M. A. I., & Nath, R. (2024). Navigating the Cyber security Landscape: A Comprehensive Review of Cyber-Attacks, Emerging Trends, and Recent Developments. World Scientific News, 190(1), 1-69. https://worldscientificnews.com/wp-content/uploads/2024/01/WSN-1901-2024-1-69-1.pdf
Manju, & Rana, C. (2024). Application of Deep Reinforcement Learning in Adversarial Malware Detection. Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real‐World Applications, 91-113.
Mohapatra, A., & Reddy, G. (2024). The implications of Artificial Intelligence (AI) on cybersecurity: A detailed review for multidomain industry. World Journal of Advanced Research and Reviews, 23(2). https://newsletter.radensa.ru/wp-content/uploads/2024/09/The_implications_of_Artificial_Intellige.pdf
Obi, O. C., Akagha, O. V., Dawodu, S. O., Anyanwu, A. C., Onwusinkwue, S., & Ahmad, I. A. I. (2024). Comprehensive review on cybersecurity: modern threats and advanced defense strategies. Computer Science & IT Research Journal, 5(2), 293-310. https://fepbl.com/index.php/csitrj/article/view/758
Olabanji, S. O., Marquis, Y., Adigwe, C. S., Ajayi, S. A., Oladoyinbo, T. O., & Olaniyi, O. O. (2024). AI-driven cloud security: Examining the impact of user behavior analysis on threat detection. Asian Journal of Research in Computer Science, 17(3), 57-74. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4709384
Palo Alto Networks. (2023). From phishing to firewalls: Solving security with AI. Retrieved from https://www.paloaltonetworks.com/blog/2023/06/solving-security-with-ai/
Ponemon Institute. (2024). The 2024 study on the state of AI in cybersecurity. Retrieved from https://ponemonsullivanreport.com/2024/04/the-2024-study-on-the-state-of-ai-in-cybersecurity/
Secureframe. (2024). How will AI affect cybersecurity? Retrieved from https://secureframe.com/blog/how-will-ai-affect-cybersecurity
Security MEA. (2024). 75% surge in cloud intrusions driven by identity-based attacks. Retrieved from https://securitymea.com/2024/07/03/75-surge-in-cloud-intrusions-driven-by-identity-based-attacks/
Shafik, W. (2024). Cyber Attacker Profiling and Cyberbullying Overview. In Cyber Space and Outer Space Security (pp. 125-149). River Publishers. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003558118-5/cyber-attacker-profiling-cyberbullying-overview-wasswa-shafik .
Siddiqi, M. A., Pak, W., & Siddiqi, M. A. (2022). A study on the psychology of social engineering-based cyberattacks and existing countermeasures. Applied Sciences, 12(12), 6042. https://www.mdpi.com/2076-3417/12/12/6042
Sophos. (2024). Ransomware payments increase 500% in the last year, finds Sophos state of ransomware report. Retrieved from https://www.sophos.com/en-us/press/press-releases/2024/04/ransomware-payments-increase-500-last-year-finds-sophos-state
Trim, P. R., & Lee, Y. I. (2021). The global cyber security model: counteracting cyber attacks through a resilient partnership arrangement. Big Data and Cognitive Computing, 5(3), 32. https://www.mdpi.com/2504-2289/5/3/32
Unger, A. (2021). Susceptibility and Response of Small Business to Cyberattacks (Master's thesis, Utica College).
Verizon. (2024). 2024 data breach investigations report. Retrieved from https://www.verizon.com/business/resources/T22a/reports/2024-dbir-data-breach-investigations-report.pdf
Vieira, B. M. C. (2024). Transforming finance: a VisionOS-Backed banking app with intelligent insights (Doctoral dissertation).
World Economic Forum. (2024). Widening disparities and growing threats cloud global cybersecurity outlook for 2024. Retrieved from https://www.weforum.org/press/2024/01/wef24-global-cybersecurity-outlook-2024/
