Implementation of AI-Powered Cybersecurity Solutions in Rwanda's Government Institutions: Case Study RISA Institution


Authors : Ezechias Irambona; Dr. Bugingo Emmanuel; Tunezerwe Emmanuel

Volume/Issue : Volume 10 - 2025, Issue 4 - April


Google Scholar : https://tinyurl.com/2dtap4f8

Scribd : https://tinyurl.com/3snjcdkh

DOI : https://doi.org/10.38124/ijisrt/25apr975

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.


Abstract : As rwanda undergoes an accelerated transition towards digitalization, ai-powered cybersecurity solutions have emerged as a critical strategy to enhance the security posture of governmental institutions. This study investigates the impact of ai on improving real-time threat detection, ensuring data protection, and boosting the overall capabilities of cybersecurity within rwanda's government sector. The findings demonstrate that ai significantly improves response times to cyber threats and strengthens data security. However, challenges such as the lack of specialized expertise and high implementation costs hinder broader adoption. Despite a high awareness of ai-driven cybersecurity solutions (93.5%), adoption rates remain low at 54.8%, revealing a gap between recognition and implementation. The study proposes a systematic approach for ai integration, emphasizing the identification of security needs, seamless integration with existing systems, and strategic planning for ai-driven security measures. The paper concludes with recommendations for policymakers and government institutions, urging the development of ai skills, increased investment in cybersecurity resources, and the creation of clear legal frameworks to address privacy concerns and prevent misuse. Future research should explore cost-effective ai solutions tailored to rwanda’s specific cybersecurity needs, enhancing adaptability and resilience.

Keywords : Artificial Intelligence, Cybersecurity, Real-Time Threat Detection, Automated Response, Data Privacy, Workforce Competency, Governmental Institutions.

References :

  1. M. A., A.-W. F. N., and H. A. M., "Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 40 environment," Cognitive Neurodynamics, 2022. [Online]. Available: https://doi.org/10.1007/s11571-022-09780-8.
  2. R. Adams and T. Harris, "Cybersecurity culture in organizations," Cybersecurity Review, 2023.
  3. T. Adams, "Leveraging AI and machine learning for real-time threat detection," Cybersecurity Advances, 2024. Ali, B. Aiswarya, B. Ezedin, and S. Khaled, "AI-powered biometrics for Internet of Things security: A review and future vision," Elsevier, vol. 23, 2024.
  4. National Cybersecurity Authority, "National Cybersecurity Strategy of Rwanda 2024-2029," p. 19, 2024. R. Baral, L. Susskind, D. J. Weitzner, and A. Wu, "Municipal cyber risk modeling using cryptographic computing to inform cyber policymaking," 2024.
  5. D. Brown and M. White, "Simulating cyberattacks for better response strategies," Cybersecurity Insights, 2023.
  6. M. Brown and C. Lee, "Operational continuity in the face of cyber threats," Network Security Review, 2023.
  7. Y. Cherdantseva et al., "A review of cyber security risk assessment methods for SCADA systems," Computers & Security, 2016.
  8. G. CISA, "Cybersecurity Governance," CISA. [Online]. Available: https://www.cisa.gov/topics/cybersecurity-best-practices/cybersecurity-governance.
  9. Cybersecurity, "U.S. GAO - Government Accountability Office," U.S. GAO. [Online]. Available: https://www.gao.gov/cybersecurity.
  10. F. D. Davis, "Perceived usefulness, perceived ease of use, and user acceptance of information technology," MIS Quarterly, 1989.
  11. F. B. Baseme, D. Hanyurwimfura, and C. Twizere, "Real-time Alert AI and IoT-based accident prevention and detection," pp. 275–286, May 30, 2024.
  12. J. Irshaad and T. O. M., "The impact of artificial intelligence on organizational cybersecurity: An outcome of a systematic literature review," Data and Information Management, pp. 3-9, 2024.
  13. M. Johnson, "Virtual infrastructure and security in modern networks," Journal of Network Security, 2024.
  14. P. Johnson and S. Miller, "Continuous monitoring for modern cybersecurity," International Cybersecurity Review, 2024.
  15. R. Johnson and C. Lee, "Response time metrics in cybersecurity drills and exercises," Cyber Defense Journal, 2023.
  16. LeewayHertz, "AI in Incident Response," Exploring Use Cases, Solutions, and Benefits. [Online]. Available: https://www.leewayhertz.com/ai-in-incident-response/.
  17. M. Taddeo, T. McCutcheon, and L. Floridi, "Trusting Artificial Intelligence in Cybersecurity Is a Double-Edged Sword," Springer International Publishing, 2021.
  18. J. Miller and T. Adams, Journal of Information Security, 2023.
  19. J. Miller and C. Lee, "Automation in cybersecurity: Enhancing team efficiency," Technology & Security, 2023.
  20. K. Roberts and A. Simmons, "Reducing detection times in modern cybersecurity systems," Cybersecurity Solutions Journal, 2023.
  21. RTSLabs, "7 Ways AI is Enhancing the Future of Data Encryption," RTSLabs. [Online]. Available: https://rtslabs.com/ways-ai-is-enhancing-data-encryption.

As rwanda undergoes an accelerated transition towards digitalization, ai-powered cybersecurity solutions have emerged as a critical strategy to enhance the security posture of governmental institutions. This study investigates the impact of ai on improving real-time threat detection, ensuring data protection, and boosting the overall capabilities of cybersecurity within rwanda's government sector. The findings demonstrate that ai significantly improves response times to cyber threats and strengthens data security. However, challenges such as the lack of specialized expertise and high implementation costs hinder broader adoption. Despite a high awareness of ai-driven cybersecurity solutions (93.5%), adoption rates remain low at 54.8%, revealing a gap between recognition and implementation. The study proposes a systematic approach for ai integration, emphasizing the identification of security needs, seamless integration with existing systems, and strategic planning for ai-driven security measures. The paper concludes with recommendations for policymakers and government institutions, urging the development of ai skills, increased investment in cybersecurity resources, and the creation of clear legal frameworks to address privacy concerns and prevent misuse. Future research should explore cost-effective ai solutions tailored to rwanda’s specific cybersecurity needs, enhancing adaptability and resilience.

Keywords : Artificial Intelligence, Cybersecurity, Real-Time Threat Detection, Automated Response, Data Privacy, Workforce Competency, Governmental Institutions.

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe