Using AI Tools on Performing Security Risk Assessment
Session
Information Systems
Description
This research paper explores the application of artificial intelligence (AI) tools in conducting security risk assessments, examining their potential to enhance the accuracy, efficiency, and effectiveness of the risk management process. The study employs a comprehensive methodology, combining a systematic literature review with expert interviews and case studies to evaluate the current state of AI implementation in risk assessment practices across various industries. The findings reveal that AI technologies significantly augment traditional risk assessment methods by rapidly processing vast amounts of data, detecting subtle patterns, and providing real-time insights. Machine learning algorithms and natural language processing capabilities enable more accurate threat detection, vulnerability analysis, and impact prediction. The research highlights the effectiveness of AI in areas such as anomaly detection, predictive analytics, and automated risk scoring. However, the study also identifies challenges in AI adoption, including data quality issues, the need for human oversight, and potential biases in AI-driven assessments. The paper emphasizes the importance of a hybrid approach that combines AI capabilities with human expertise to leverage the strengths of both. The research concludes that while AI tools offer substantial benefits in performing security risk assessments, their successful implementation requires careful consideration of organizational context, data governance, and ethical implications. The paper provides recommendations for practitioners and policymakers on effectively integrating AI into existing risk management frameworks and suggests areas for future research to address current limitations and emerging challenges in the field
Keywords:
information security, AI tools, Security risk assessment, Artificial intelligence, Risk management, Machine learning, Cybersecurity
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-982-15-3
Location
UBT Kampus, Lipjan
Start Date
25-10-2024 9:00 AM
End Date
27-10-2024 6:00 PM
DOI
10.33107/ubt-ic.2024.288
Recommended Citation
Abazi, Blerton, "Using AI Tools on Performing Security Risk Assessment" (2024). UBT International Conference. 1.
https://knowledgecenter.ubt-uni.net/conference/2024UBTIC/IS/1
Using AI Tools on Performing Security Risk Assessment
UBT Kampus, Lipjan
This research paper explores the application of artificial intelligence (AI) tools in conducting security risk assessments, examining their potential to enhance the accuracy, efficiency, and effectiveness of the risk management process. The study employs a comprehensive methodology, combining a systematic literature review with expert interviews and case studies to evaluate the current state of AI implementation in risk assessment practices across various industries. The findings reveal that AI technologies significantly augment traditional risk assessment methods by rapidly processing vast amounts of data, detecting subtle patterns, and providing real-time insights. Machine learning algorithms and natural language processing capabilities enable more accurate threat detection, vulnerability analysis, and impact prediction. The research highlights the effectiveness of AI in areas such as anomaly detection, predictive analytics, and automated risk scoring. However, the study also identifies challenges in AI adoption, including data quality issues, the need for human oversight, and potential biases in AI-driven assessments. The paper emphasizes the importance of a hybrid approach that combines AI capabilities with human expertise to leverage the strengths of both. The research concludes that while AI tools offer substantial benefits in performing security risk assessments, their successful implementation requires careful consideration of organizational context, data governance, and ethical implications. The paper provides recommendations for practitioners and policymakers on effectively integrating AI into existing risk management frameworks and suggests areas for future research to address current limitations and emerging challenges in the field