AI-Driven Threat Detection for Public Sector Systems: Balancing Innovation and Privacy
Seth Nti Berko
Abstract
The integration of artificial intelligence (AI) into cybersecurity systems has revolutionized threat detection capabilities within public sector organizations. As government agencies increasingly rely on digital infrastructure to deliver essential services, they face sophisticated cyber threats that traditional security measures struggle to address. This study examines the implementation of AI-driven threat detection systems in public sector environments, with particular emphasis on the critical balance between technological innovation and privacy preservation. Through a comprehensive review of current literature and analysis of existing frameworks, this research explores how machine learning and deep learning approaches enhance intrusion detection while maintaining compliance with data protection regulations. The findings reveal that federated learning, differential privacy mechanisms, and explainable AI techniques offer promising pathways for deploying robust threat detection systems that respect citizen privacy. However, challenges persist in addressing adversarial attacks, ensuring algorithmic fairness, and maintaining transparency in automated decision-making processes. This study contributes to the growing body of knowledge on responsible AI deployment in security-critical environments and provides practical recommendations for public sector organizations seeking to modernize their cybersecurity infrastructure without compromising fundamental privacy rights.
Keywords
Artificial Intelligence
Threat Detection
Public Sector Cybersecurity
Privacy-Preserving Machine Learning
Federated Learning
Differential Privacy
Explainable AI
Intrusion Detection Systems
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systems: A survey. Applied Sciences, 9(20), 4396. https://doi.org/10.3390/app9204396
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Management
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1.0).
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and
privacy
controls
for
information
systems
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organizations.
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applying
the
risk
management
framework
to
federal
information
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and
opportunities.
IEEE
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10,
112392–112415.
https://doi.org/10.1109/ACCESS.2022.3216617
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of threats and countermeasures. EURASIP Journal on Information Security, 2024, 10.
https://doi.org/10.1186/s13635-024-00158-3
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systems based on machine learning techniques for the protection of critical infrastructure.
Sensors, 23(5), 2415. https://doi.org/10.3390/s23052415
RodrÃguez-Barroso, N., Stipcich, G., Jiménez-López, D., Ruiz-Millán, J. A., MartÃnez-Cámara, E.,
González-Seco, G., Luzón, M. V., Veganzones, M. A., & Herrera, F. (2024). Federated
learning and differential privacy: Software tools analysis, the Sherpa.ai FL framework and
methodological guidelines for preserving data privacy. Information Fusion, 64, 270–292.
https://doi.org/10.1016/j.inffus.2020.07.009
Sarker, I. H. (2023). AI-driven cybersecurity: An overview, security intelligence modeling and
research directions. SN Computer Science, 4, 173. https://doi.org/10.1007/s42979-023-
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Vassilev, A., Oprea, A., Fordyce, A., & Anderson, H. (2021). Adversarial machine learning: A
taxonomy and terminology of attacks and mitigations. NIST AI 100-2e2023.
https://doi.org/10.6028/NIST.AI.100-2e2023
Wang, L., Zhang, Y., & Li, H. (2024). Explainable AI-based innovative hybrid ensemble model
for intrusion detection systems. Journal of Cloud Computing: Advances, Systems and
Applications, 13, 71. https://doi.org/10.1186/s13677-024-00712-x
Wang, S., Zhang, Y., & Chen, L. (2024). Explainable intrusion detection systems using LIME and
SHAP: A comparative study on machine learning models. IEEE Access, 11, 89625–89643.
https://doi.org/10.1109/ACCESS.2023.3305678
Yang, M., Guo, T., Zhu, T., Tjuawinata, I., Zhao, J., & Lam, K.-Y. (2023). Local differential
privacy and its applications: A comprehensive survey. Computer Standards & Interfaces,
89, 103827. https://doi.org/10.1016/j.csi.2023.103827
