Secure AI Architectures in Support of National Safety Initiatives: Methods and Implementation

Authors

  • Priya Dharshini Kalyanasundaram Amazon, USA Author
  • Debasish Paul JPMorgan Chase, USA Author

Keywords:

secure AI architectures, national safety, operational security, privacy compliance, machine learning frameworks

Abstract

By ensuring robust operational security, privacy compliance, and regulatory alignment plays a critical role in advancing national safety initiatives by using Secure artificial intelligence (AI) architectures. The aim of this research is to present a comprehensive analysis of methodologies support the design and implementation of secure AI-driven safety platforms, with a focus on Amazon’s advanced AI infrastructure.

Downloads

Download data is not yet available.

References

J. K. Liu, "Artificial intelligence for national security: Applications, challenges, and prospects," IEEE Access, vol. 8, pp. 14294-14312, 2020.

P. A. Wilson and S. B. Zhang, "Designing secure AI systems for national defense and security applications," IEEE Transactions on Information Forensics and Security, vol. 15, no. 4, pp. 1145-1157, 2021.

A. D. Nguyen, H. S. Kim, and T. J. Lee, "Privacy-preserving AI algorithms for national security," IEEE Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 115-128, 2021.

L. Zhao and Q. Yu, "Quantum computing and its applications in AI security for defense and law enforcement," IEEE Transactions on Quantum Engineering, vol. 4, no. 1, pp. 28-40, 2022.

S. J. Lee, M. G. Kim, and H. J. Park, "Federated learning for privacy-preserving AI in security applications," IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3727-3735, 2021.

A. K. Gupta, "AI-driven risk analysis and decision-making in national security systems," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 1, pp. 167-178, 2021.

F. R. Patel, M. P. Gupta, and A. M. Singh, "Secure AI architectures: A survey on privacy and compliance frameworks," IEEE Transactions on Cloud Computing, vol. 8, no. 3, pp. 734-747, 2020.

M. E. Owens, D. J. Harris, and D. B. Foster, "Adversarial machine learning for securing AI-based systems in national safety contexts," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 2, pp. 1124-1137, 2021.

S. L. Davis and R. W. Thompson, "AI-powered autonomous systems in national defense: A case study on resilience and vulnerability," IEEE Transactions on Automation Science and Engineering, vol. 18, no. 5, pp. 2760-2772, 2022.

A. C. Miller and S. G. Roberts, "Securing AI-driven platforms for disaster response and management," IEEE Transactions on Computational Social Systems, vol. 9, no. 1, pp. 45-58, 2020.

X. B. Chen, Y. H. Li, and Z. S. Wang, "Exploring the role of machine learning in predictive security systems for national safety," IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 3, pp. 500-514, 2021.

J. S. Liao and H. S. Chang, "Challenges in scaling AI systems for national security applications," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 6, pp. 2738-2749, 2020.

D. H. Williams and P. N. White, "AI-based anomaly detection for cyber threat mitigation in critical infrastructure," IEEE Transactions on Industrial Informatics, vol. 16, no. 8, pp. 5448-5457, 2020.

K. W. Stewart, A. P. Barker, and E. G. Monroe, "Addressing AI security challenges in the context of federal law enforcement," IEEE Security and Privacy, vol. 18, no. 3, pp. 50-58, 2021.

F. A. Lee, T. A. Haskell, and B. C. Adams, "Blockchain for securing AI models in critical national infrastructure," IEEE Transactions on Blockchain Technology, vol. 2, no. 1, pp. 18-29, 2021.

S. D. Zhang and J. M. Barrows, "Cloud-based security architectures for large-scale AI systems in national safety applications," IEEE Transactions on Cloud Computing, vol. 9, no. 4, pp. 1032-1043, 2022.

L. B. Richards and H. R. Hawkins, "A survey on privacy-preserving techniques for AI-driven surveillance systems," IEEE Transactions on Privacy and Security, vol. 21, no. 7, pp. 1018-1030, 2021.

P. R. Patel and T. K. Agarwal, "Security and compliance in AI-based surveillance and predictive policing," IEEE Transactions on Security and Privacy, vol. 16, no. 9, pp. 2452-2464, 2021.

M. G. Valiant, R. G. Sharma, and J. B. Gupta, "AI ethics and responsible deployment of AI in national security applications," IEEE Transactions on Technology and Society, vol. 2, no. 2, pp. 99-111, 2022.

L. J. Morris, S. K. Williams, and A. T. Roberts, "AI resilience and cybersecurity in autonomous systems for national defense," IEEE Transactions on Industrial Electronics, vol. 68, no. 4, pp. 2980-2992, 2021.

Downloads

Published

12-12-2023

How to Cite

[1]
Priya Dharshini Kalyanasundaram and Debasish Paul, “Secure AI Architectures in Support of National Safety Initiatives: Methods and Implementation”, Newark J. Hum. Centric AI Robot Inter., vol. 3, pp. 322–355, Dec. 2023, Accessed: Dec. 21, 2025. [Online]. Available: https://njhcair.org/index.php/publication/article/view/34