Development of an AI-Based Risk Assessment System for University Cybersecurity: A Case Study of Federal University, Birnin Kebbi (Birnin Kebbi LGA, Kebbi State)
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Background of the Study
As universities continue to embrace digital transformation, the need for robust cybersecurity systems becomes more critical. Universities handle vast amounts of sensitive data, ranging from student records to research data and financial transactions, making them attractive targets for cyberattacks (Ademola & Onifade, 2024). Traditional cybersecurity measures, such as firewalls and antivirus software, are often insufficient in the face of advanced cyber threats. In response, AI-based risk assessment systems have emerged as a promising solution for detecting, analyzing, and mitigating cybersecurity risks in real-time (Eze et al., 2023). These systems use machine learning and data analytics to predict potential vulnerabilities, assess threats, and recommend appropriate countermeasures. By continuously learning from new data and past incidents, AI systems can provide dynamic, adaptive protection against evolving cybersecurity threats (Madu et al., 2024).
At Federal University, Birnin Kebbi, located in Birnin Kebbi LGA, Kebbi State, cybersecurity challenges are exacerbated by the increasing use of online platforms for teaching, learning, and administrative functions. The university’s reliance on digital systems for storing sensitive data makes it vulnerable to cyberattacks, which can lead to significant financial and reputational damage. This study seeks to explore the potential of AI-based risk assessment systems in enhancing the university’s cybersecurity by identifying and mitigating risks before they can lead to data breaches or other security incidents.
Statement of the Problem
Federal University, Birnin Kebbi faces numerous cybersecurity challenges due to its growing reliance on digital technologies. Traditional methods of managing cybersecurity risks are reactive, often addressing threats after they have already impacted the system. The university requires a proactive and dynamic approach to identify and address potential vulnerabilities before they lead to significant consequences. AI-based risk assessment systems, while promising, have not been fully implemented or evaluated in the university's cybersecurity framework, necessitating further investigation into their effectiveness.
Objectives of the Study
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To develop an AI-based risk assessment system for improving cybersecurity at Federal University, Birnin Kebbi.
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To assess the effectiveness of the AI-based system in identifying and mitigating cybersecurity risks at the university.
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To evaluate the impact of the AI-based system on enhancing the university’s overall cybersecurity posture.
Research Questions
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How effective is the AI-based risk assessment system in identifying cybersecurity threats at Federal University, Birnin Kebbi?
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What impact does the AI-based system have on mitigating cybersecurity risks at the university?
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How can the AI-based system improve the university’s response to emerging cybersecurity threats?
Significance of the Study
This study will provide valuable insights into the role of AI in enhancing university cybersecurity. By implementing an AI-based risk assessment system, Federal University, Birnin Kebbi can improve its ability to predict, prevent, and mitigate cyber threats, thereby safeguarding its data and resources. The findings may serve as a model for other universities seeking to bolster their cybersecurity defenses.
Scope and Limitations of the Study
The study will focus on developing and evaluating an AI-based risk assessment system for cybersecurity at Federal University, Birnin Kebbi, located in Birnin Kebbi LGA, Kebbi State. The research will focus solely on the effectiveness of the AI system in identifying cybersecurity risks and will not explore broader IT infrastructure or policy-related issues.
Definitions of Terms
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AI-Based Risk Assessment System: A system that uses artificial intelligence to identify, analyze, and assess risks in real-time, providing actionable insights for mitigating potential threats.
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Cybersecurity: The practice of protecting computer systems, networks, and data from unauthorized access, attacks, or damage.
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Machine Learning: A type of artificial intelligence that enables systems to learn from data and improve their performance over time without being explicitly programmed.
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