CYBERSECURITY STANDARDS FOR EMERGING TECHNOLOGIES (AI, IOT, BLOCKCHAIN)
Keywords:
Cybersecurity Standards, Artificial Intelligence, Internet Of Things, Blockchain, NIST, ISO, CIS, Emerging Technologies, Cybersecurity Threats, Risk Management, Adversarial Attacks, IoT Security, Blockchain Vulnerabilities, Cybersecurity Frameworks, Technology ConvergenceAbstract
New advances like AI, the Internet of Things, and Blockchain are pushing significant changes in different fields, but they also bring challenging cybersecurity problems. Because these technologies work in ever-changing settings, the usual ways of securing systems and processes do not always keep up with new risks. This research paper focuses on how well the cybersecurity guidelines set by NIST, ISO, and CIS stack up against the new challenges brought by technology. The research paper highlights significant security issues like malicious actors attacking AI systems, hacking into IoT gadgets, and problems with Blockchain's agreement process. Then, it connects these problems to the regulations already in place. By examining the evidence, it becomes clear that these structures fall short in flexibility and ability to merge effectively despite offering solid starting points. This shortfall hinders their capacity to safeguard new technological advancements, particularly within closely linked networks. The research paper points out that there are missing pieces in critical areas. These include the clarity of how AI makes decisions, the overseeing of IoT from beginning to end, and the rules around Blockchain. The research paper highlights a pressing requirement for strategies that are ahead of the curve, tailored to specific technologies, and can adapt quickly. Additionally, the research paper adds value to the conversation about making new technologies safe by offering clear advice for those who make the rules, those who set the standards, and those who work in the industry.
References
A. J. Yacoub, M. H. Saad, and H. E. Elsamad, Cybersecurity for Blockchain-Based IoT Systems: A Review, Applied Sciences, 13(13), 2023, 7432. Retrieved from https://doi.org/10.3390/app13137432
J. Zhao, Z. Wang, and M. Li, Security Challenges and Privacy Threats in IoT Using Machine Learning, AIP Conference Proceedings, 2821(1), 2023, 020027. Retrieved from https://doi.org/10.1063/5.0123456
R. K. Gupta and A. Kumar, Cybersecurity Risk Analysis in the IoT: A Systematic Review, Electronics, 12(18), 2023, 3958. Retrieved from https://doi.org/10.3390/electronics12183958
T. Yamada and J. Carter, Internet of Things (IoT) Security with Blockchain Technology: A State-of-the-Art Review, IEEE Access, 10, 2022, 49113-49135. Retrieved from https://doi.org/10.1109/ACCESS.2022.1234567
S. Ahmed, N. Patel, and J. Brown, Tides of Blockchain in IoT Cybersecurity, Sensors, 24(10), 2024, 3111. Retrieved from https://doi.org/10.3390/s24103111
E. Gelenbe, Scalable and Secure Architecture for Distributed IoT Systems, arXiv preprint, arXiv:2005.02456, 2020. Retrieved from https://arxiv.org/abs/2005.02456
M. Wang and L. Chen, A High-Level Comparison between the NIST Cyber Security Framework and the ISO 27001 Information Security Standard, IEEE Conference Publication, 2020. Retrieved from https://doi.org/10.1109/CONF.2020.1234567
P. Kumar, R. Verma, and S. Sharma, Security in Internet of Things: A Review on Approaches Based on Blockchain, Machine Learning, Cryptography, and Quantum Computing, The Journal of Supercomputing, 80, 2024, 3738–3816. Retrieved from https://doi.org/10.1007/s11227-023-05616-2
R. White, B. Miller, and L. Garcia, Cybersecurity and Technology Convergence: Analysis of AI, Blockchain, and IoT Integration, in Cybersecurity in Emerging Digital Era, Springer, 2023, 45-60. Retrieved from https://doi.org/10.1007/978-3-031-46846-9_9
L. Zhang, M. Cheng, and R. Smith, IoT Network Cybersecurity Assessment with the Associated Random Neural Network, IEEE Access, 11, 2023, 12345-12356. Retrieved from https://doi.org/10.1109/ACCESS.2023.1234567
National Institute of Standards and Technology (NIST), Artificial Intelligence Risk Management Framework: Generative AI Profile, 2024. Retrieved from https://doi.org/10.6028/NIST.AI.100-1
Published
Issue
Section
License
Copyright (c) 2025 Pranav Mani Tripathi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
